ZVENIA Mining

En minería, los números lindos en un PowerPoint no garantizan que un proyecto sea viable. Lo que define si una mina existe o no, está en los detalles técnicos que muchas veces se pasan por alto. Estos conceptos son la base para evaluar si un proyecto es sólido o si solo tiene potencial geológico sin sustento económico real. Entenderlos te permite reducir riesgos, hacer mejores preguntas y tomar decisiones con información técnica, no solo con promesas. 📌 Resumen clave (checklist para inversores): ✅ Preguntá por reservas, no solo recursos. ✅ Entendé la ley de corte y cómo cambia con precios/costos. ✅ Exigí modelos con dilución y recuperación realistas. ✅ Evaluá CAPEX con contingencias y OPEX con benchmarks. ✅ Verificá experiencia del equipo en ese tipo de depósito. ✅ Validá que haya un QP independiente certificado revisando el proyecto. ¿Cuál de estos conceptos te resultó más útil? ¿Hay alguno que agregarías? Si estás evaluando un proyecto minero y necesitás una segunda opinión técnica independiente (NI 43-101, due diligence, CPR), escribime. Puedo ayudarte a separar proyectos sólidos de presentaciones de PowerPoint.

Key Takeaways The U.S. is 100% import reliant for several critical minerals used in AI-related infrastructure. Core data center components—from circuitry to magnets—depend heavily on foreign-sourced materials. The artificial intelligence boom is driving an unprecedented buildout of data centers across the United States. Behind every AI model and cloud server sits a complex web of minerals that make modern computing possible. From semiconductors to cooling systems, these materials form the backbone of digital infrastructure. This visualization breaks down the critical minerals used in AI data centers—and how reliant the U.S. is on imports for each. The data for this visualization comes from the U.S. Geological Survey (USGS). Semiconductors: America’s Biggest Vulnerability Semiconductors are the “brains” of AI data centers—and they are highly import dependent. The U.S. is 100% reliant on imports for arsenic, fluorspar, gallium, germanium, indium, and tantalum used in chip production. It also imports 85% of its platinum and 36% of its palladium needs, both critical for chip manufacturing. While silicon, the base material for chips, has less than 50% import reliance, many of the trace elements that enable advanced computing are entirely foreign-controlled. Data center component Critical mineral U.S. import reliance (%) Server boards and circuitry Silver 64% Gold 0% Copper 45% Tin 73% Tantalum 100% Palladium 36% Platinum 85% Heat sinks Aluminum 47% Copper 45% Semiconductors and microchips Arsenic 100% Fluorspar 100% Gallium 100% Germanium 100% Indium 100% Palladium 36% Platinum 85% Silicon 75% Boron 0% Rare earth elements 80% Circuitry and Server Components Beyond chips, server boards and circuitry require a range of conductive and precious metals. The U.S. imports 64% of its silver and 73% of its tin, both vital for soldering and electrical conductivity. Copper—essential for wiring and connectivity—has a 45% import reliance. Tantalum, used in capacitors, is 100% imported. Gold stands out as a rare exception, with 0% net import reliance, offering a small pocket of domestic security in an otherwise globalized supply chain. Cooling Systems and Data Storage AI servers generate massive heat loads, making cooling systems crucial. Heat sinks rely on aluminum (47% import reliance) and copper (45%). Meanwhile, data storage components such as magnets and drives depend on rare earth elements, with 80% import reliance. Barite—used in storage-related applications—has also more than 75% reliance. China’s Commanding Share Currently, China dominates the production of most of the critical minerals used in data centers. This near-monopoly has become a major concern for other nations, with the U.S. government currently pushing for increased domestic production of these materials. In addition to being the leading producer, China also controls much of the refining capacity for many of these minerals. For example, around 90% of rare earths are refined in China. In the race to dominate AI, access to critical minerals may prove just as important as technological leadership.

For your team • Richer brainstorming sessions • Smoother conflict resolution • Personalized development • Fewer misunderstandings • Improved communication • Creative problem-solving • Stronger team cohesion • Efficient work allocation • Reduced stress levels • Greater engagement For you: • Greater resilience • More adaptability • Stronger influence • Enhanced empathy • Deeper connections • Heightened intuition • Increased confidence • Better self-awareness • Clearer communication • Sharper decision-making Every great leader knows: Understanding your team is the first step to success. Why this matters? It reveals how each team member: ➟ Thinks and processes information ➟ Interacts and communicates ➟ Responds to stress and conflict Armed with this knowledge, you can: ➟ Tailor your leadership style ➟ Enhance team synergy ➟ Boost overall productivity Save this sheet. Share it with your team. Use it as your daily reference. P.S. Which personality model do you find most useful? 📌 Want a PDF of my Personality Types cheat sheet + 100 more leadership resources? Get them free: https://ericpartaker.com/

In underground mining operations worldwide, gradeline establishment and control is a fundamental responsibility of the Mine Surveyor, not just a site marking exercise. As Mine Surveyors, we are responsible for translating approved mine designs, levels, and gradients into the underground environment in accordance with global mining standards and best practice (ICMM principles and internationally recognised QA/QC systems). The gradeline shown here supports key surveying and operational outcomes: 🔹 Accurate development control – Ensuring drives, declines, and crosscuts are excavated to design line and level 🔹 Design compliance and verification – Providing a continuous visual reference for checking conformance and identifying deviations early 🔹 Effective water management – Maintaining correct gradients for drainage and long-term asset integrity 🔹 Safety and risk management – Reducing overbreak, underbreak, rework, and geotechnical exposure 🔹 Operational consistency – Enabling clear communication of survey intent across crews, shifts, and contractors In constrained underground environments, gradelines represent the direct interface between survey control, engineering intent, and excavation execution. Their accuracy directly reflects the quality, accountability, and professionalism of the Mine Survey function. Whether operating in Africa, Australia, or any global mining jurisdiction, precise gradeline control remains a core surveying duty that underpins safe, efficient, and compliant underground mining.

Dans les opérations minières, la qualité des données est aussi importante que la production elle-même. En grade control, le QAQC (Quality Assurance & Quality Control) joue un rôle essentiel pour garantir la fiabilité des teneurs et sécuriser la prise de décision. 🔎 Pourquoi le QAQC est indispensable ? • Assurer la précision des résultats analytiques • Détecter rapidement les erreurs d’échantillonnage ou de laboratoire • Réduire les risques financiers liés aux mauvaises estimations de teneur • Renforcer la confiance entre terrain, laboratoire et géologie 🧪 Les outils clés du QAQC : ✔️ Blanks – pour vérifier la contamination ✔️ Standards/CRM – pour contrôler la précision des analyses ✔️ Duplicates – pour évaluer la reproductibilité ✔️ Suivi des tendances et validation des lots avant utilisation Un programme QAQC bien appliqué permet de transformer des données brutes en informations fiables, essentielles pour optimiser l’exploitation et réduire les incertitudes. En mine, de bonnes décisions commencent toujours par de bonnes données.

Consequent to a previous article, we have been asked to discuss briefly the financial implications of poor drilling. When we talk about drill and blast, the conversation usually leans towards explosives and their application, while the drilling component gets far less attention. Many operations still track drilling the way they always have: cost per metre drilled. It’s neat. It’s familiar, yet it’s dangerously incomplete. Drilling doesn’t exist in isolation. Every metre drilled sets the conditions for blast performance, downstream productivity, and ultimately profitability. When drilling is optimised solely to reduce the drill contractor’s invoice, costs are often pushed—quietly but significantly—into every stage that follows. That’s why Total Drilling Cost matters far more than drill cost alone. What Is TDC (Really)? Total drilling cost is not just: Drill contractor rates Consumables Maintenance Fuel and labour It also includes the consequences of drilling quality: Hole deviation and collar accuracy Burden and spacing variability Sub-drill consistency Redrilling Misfires and blast inefficiencies Poor fragmentation impacting loading, hauling,crushing Increased dilution, losses, or downstream bottlenecks A cheap metre drilled badly is one of the most expensive metres you’ll ever mine. The False Economy of “Cheap Drilling” We've seen this pattern repeatedly: Drill rates are pushed down Penetration rate becomes the primary KPI QA/QC is reduced Operators are incentivised on metres, not quality On paper, drilling cost improves. In reality: Blast performance becomes inconsistent Powder factor increases to compensate Dig rates slow Crusher throughput drops Maintenance costs rise Reconciliation gaps widen The operation spends far more trying to fix the blast than it saved on drilling. Shifting the Conversation: From Cost to Value High-performing operations look at drilling differently: Cost per effective hole, not cost per metre Accuracy compliance, not production metres Drill-to-blast alignment, not isolated KPIs System performance, not departmental optimisation They understand that drilling is a precision activity, not a volume race. What to Measure Instead If you want to understand your true drilling cost, ask: How much rework are we carrying due to poor hole placement? What is the variance between planned and achieved burden? How often do blast designs require modification to compensate for drilling? What is the downstream cost of poor fragmentation? Are drill KPIs aligned with blast outcomes—or working against them? These answers rarely sit in one department. Final Thought Drilling is the first act in value creation. Get it right, and everything downstream becomes easier. Get it wrong, and no amount of blasting “optimisation” will save you. If you’re still judging drilling purely on cost per metre, you’re likely paying far more than you think.

This side-by-side comparison shows just how quickly mining equipment scales, and why lumping machines together misses the reality of modern operations. A mine-spec Toyota LandCruiser is designed for mobility, inspections, and supervision. It weighs just over a tonne and operates in a low-consequence environment. Step into a CAT 777 and you are already dealing with a 90-plus tonne machine where braking distance, payload discipline, and ground conditions matter. Move up through the 785, 789 and 793 classes and each jump brings exponential increases in size, energy, tyre loads, maintenance complexity, and safety exposure. By the time you reach the ultra-class CAT 797, you are in a completely different operating category. At more than 360 tonnes, this truck does not simply service a mine, it defines it. Haul roads, pit geometry, maintenance workshops, fuel systems, and training frameworks are all engineered around equipment of this scale. This is why ultra-class fleets are typically found at tier-one operations run by miners such as BHP, Rio Tinto, Fortescue, Newmont and Vale. These machines only make sense where ore bodies are large, mine lives are long, and operational discipline is non-negotiable. The takeaway is simple. As equipment scales, so do the operating environments around it. Light vehicles, mid-class haul trucks and ultra-class fleets sit on a continuum of size, consequence and complexity, each requiring different levels of planning, support and experience. Scale does not change the fundamentals of mining, but it significantly changes how they must be managed.

Estimados Amigos y Colegas a través de la presente es grato saludarlos así mismo quiero compartir con ustedes un trabajo desarrollado en el Curso de Fundamentos en Economía de Minerales III-2024, denominado: Errors and Uncertainty in Mineral Resource and Ore Reserve Estimation: The Importance of Getting it Right, se trata de un análisis sobre cómo gestionar los errores e incertidumbres identificados en el proceso de estimación de recursos y reservas de mineral según las regulaciones internacionales.

From Hoshin to Gemba: Japanese KMI → KPI → KAI in Action In many companies, performance management quickly becomes a jungle of disconnected numbers. Japanese management philosophy offers a very simple and powerful cascade that solves this: KMI → KPI → KAI ✅ KMI (Key Management Indicator): The “WHY” KMIs are the few strategic indicators owned by top management. They describe what really matters for business over the term such as: 🎯 Safety, Health & Environment 🎯 Customer Engagement & Quality 🎯 People & Culture 🎯 Asset Utilization 🎯 Cost & Profitability Think of KMIs as the North Stars on your dashboard : there should be only a few ✅ KPI (Key Performance Indicator): The “WHAT” KPIs translate each KMI into measurable process metric. One KMI is usually supported by several KPIs, but total set should still be lean (10 max for site). Examples : Safety, Health & Environment: -TRIR, Days Without Incident, Emissions Asset Utilization: - OEE, Throughput, Capacity Utilization Customer Engagement & Quality: - RFT, Claims, OTIF Cost & Productivity : - Unit Cost, Inventory Turnover, COPQ, Energy Consumption People & Culture : - eNPS, Absenteeism, Turnover, Suggestions ✅ KAI (Key Activity Indicator): The “HOW” KAI's are specific actions at shop‑floor level that move the KPIs. They are concrete and small enough to be owned by a team. Examples : To improve OEE; KAIs could be: - Setup time ↓ - # of Breakdowns ↓ - MTBF ↑ - Short stops # ↓ To improve TRIR, KAIs could be: - Observations # ↑ - Safety audits # ↑ - PPE compliance % ↑ To improve Suggestions, KAIs could be: - Ideas/FTE ↑ - Implementation % ↑ - Idea cycle days ↓ The KAI key is ‘Activity’, not a result. Why KMI → KPI → KAI Cascade 1️⃣ Aligns strategy to Gemba Everyone can see how today’s kaizens, suggestions, or project contributes all the way up to Safety, Customer, Cost, or People objectives 2️⃣ Clarifies target setting You start with few KMIs, translate them into focused KPIs, then define KAIs, teams can influence every day 3️⃣ Enables performance management, not just reporting Reviews can follow simple logic: KMI trend ↓ → which KPI is off? → which KAI needs to improve/change? 4️⃣ Drives Continuous Improvement KAI's become natural funnel for Kaizens: If KPI is red, teams propose and track more Kaizens until it turns green. How to implement ✅ Start by agreeing on 4–5 KMIs that truly define success for your site ✅ Limit yourself to max 10 KPIs, clearly owned, each mapped to one KMI ✅ For each KPI, define 2–3 KAIs, specific activities you want teams to execute ✅ Review regularly KMI → KPI → KAI on the same page, as in visual, so everyone sees connection When you get this right, you create a clear line of sight: KAI improves KPI → KPI improves KMI → Every team, Every shift, Every individual, Every improvement contributes directly to the company’s success. *Plase follow + like + leave a comment (NOT an email address) & message me within LinkedIn for file

Every mine operates within a defined timeframe known as the Life of Mine (LoM). From the first blast to the final tonne and eventual closure, LoM planning ensures that mining is carried out efficiently, safely, and profitably over the entire lifespan of the operation. This week, we explore what Life of Mine means and why it is one of the most important concepts in mining engineering. ⏳ What Is Life of Mine (LoM)? Life of Mine refers to the total duration over which a mineral deposit can be economically extracted, based on available reserves, production rates, and economic conditions. LoM is not just a number of years — it is a strategic plan. 🧩 Key Components of Life of Mine Planning 1. Resource & Reserve Base Defines how much material is available for extraction. 2. Production Rate Determines how fast the ore is mined and processed. 3. Mining & Processing Strategy Choice of mining method, equipment, and processing route. 4. Economic Assumptions Commodity prices, costs, and cut-off grade. 5. Closure & Rehabilitation Planning End-of-mine considerations integrated from the start. ⚙️ Why Life of Mine Matters 📈 Guides long-term investment decisions 💰 Influences cash flow and project valuation 🛠️ Aligns short-term plans with long-term goals 🌱 Integrates sustainability and closure from day one 📊 Helps manage risk and uncertainty over time A well-defined Life of Mine is the backbone of responsible and profitable mining. 💬 Do you think Life of Mine estimates should be conservative or optimistic? Share your thoughts. If this added value, like, comment, follow, and repost to support mining education.

Mining executives are too distracted to fix operations. Technology is not the answer. Surprising to hear from someone who markets mining technology, I know. But hear me out. Mining tech conferences are packed these days. Vendors pitch AI dashboards, autonomous trucks, and predictive-maintenance platforms. Boardrooms nod along, eager not to fall behind. Yet when Bain* recently asked executives from miners worth a combined $300bn to rank the factors most critical to improving site operations, technology came dead last. What topped the list was considerably less photogenic. Buy-in from the front line and management came first, followed by a strong understanding of what actually creates value and, in third place, stable leadership. The executives were saying that without the human scaffolding, even the cleverest tools amount to expensive furniture. Technology works just fine. The software does what it promises. The problem is rarely the tool. The problem is the system underneath it. And if that system is flawed, if decision rights are murky, if leadership turns over every eighteen months, if nobody on the front line was consulted before the rollout, then no amount of technology will fix it. You are just automating a broken process. You have to start from the beginning: people. The more conversations I have with stakeholders across this industry, the clearer a pattern becomes. There are two ways to bring innovation into mining. The first, which most choose, is to adapt to the existing audience. Sell to them the way they are used to buying. This is why mining tech startups begin with a consulting model before attempting to shift towards subscription or platform plays. It is pragmatic. It gets you in the door. But it also means you end up reinforcing the very system you set out to improve. The second way is to try to shift the industry's thinking altogether. This is harder, slower, and impossible for any single company or person to pull off alone. It requires a collective effort from operators, vendors, investors, and the growing number of voices in this sector who see that the old way of doing things is running out of road. The pressure to produce more from less is not going away, and adapting to an outdated operating model will not meet it. If you are a tech vendor, your leverage is limited. You build the best product you can and hope the buyer is ready for it. But there are plenty of people across this industry with platforms, experience, and credibility who could help change the narrative. Mining's greatest untapped resource remains the one that has always been there: people who know the operation, who stay long enough to see changes through, and who are trusted enough to be told the truth when things go wrong. The industry would do well to invest in them with the same enthusiasm it reserves for the latest AI. Whether enough of those voices choose to speak up is another question.

Reviewing mining projects from discovery through to operations, there’s one issue that comes up time and time again: resource classification. Teams spend countless hours on estimation methods and geological interpretation, only to fall short when it comes to correctly classifying the resource. Why does it happen? Optimism bias (often driven by board or funding expectations) Time pressure at the end of a study Inexperience with classification standards Or simply underestimating how critical classification really is As an external reviewer, here are a few practical principles that consistently separate robust resource models from risky ones: 🔹 Let drill spacing lead the classification Be strict. If there are spacing gaps, classify them accordingly. No “spotted dogs”. 🔹 Avoid circles around drill holes Resources aren’t radial. Use orebody knowledge and geological controls to define realistic shapes. 🔹 Data integrity matters QAQC results, core recovery, and missing samples must be reflected in the classification. Confidence in tonnes starts with confidence in data. 🔹 Respect the limits of your estimation parameters Search radii, variography, and interpolation choices should cap — not inflate — classification confidence. 🔹 Classify waste properly If the drill spacing supports a measured ore classification, the same logic applies to internal waste. Mining companies want to know where measured waste is — ironically, we mine far more waste than ore. Resource classification isn’t a tick-box exercise. It’s a risk statement, and decision-makers rely on it more than we often acknowledge. Get it right early, and everything downstream — mine design, scheduling, economics — becomes stronger. Happy to discuss or hear how others approach this challenge in their projects.

A short post to remind mine planning engineers that grade is just a proxy for value. You need to prove to yourself that it is a good proxy to use. Years ago, I used to present a value creation and #CutOffGrade seminar. I would start the seminar with the following three questions as a thinking exercise: Q1: Consider two blocks of ore (everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu. (b) 100 tonnes @ 2.5% Cu. The obvious answer would be (b), as it contains 2.5 tonnes of copper, versus (a) with 2.0 tonnes of contained copper Q2: Consider the same two blocks of ore, but now with recovery information (and again everything else being equal), which block has higher value:: (a) 100 tonnes @ 2.0% Cu - with 80% recovery. (b) 100 tonnes @ 2.5% Cu – with 85% recovery. Again, the obvious answer would be (b), as it has 2.1 tonnes of recovered copper versus (a) with 1.6 tonnes of recovered copper. Q3: Now consider the same two blocks of ore, with recovery information and throughput information (and again everything else being equal), which block has higher value: (a) 100 tonnes @ 2.0% Cu - with 80% recovery, and SAG mill throughput of 100 tph (so soft ore, and/ore well fragmented). (b) 100 tonnes @ 2.5% Cu – with 85% recovery, and SAG mill throughput of 70 tph (so hard ore, and/or poorly fragmented). Now, the answer flips to (a), as (a) has a recovered copper per hour ‘value’ of 1.6 tonnes Cu per hour, and (b) has a ‘value’ of 1.49 tonnes Cu per hour. So, in this third situation, the lower grade, lower recovery ore provides 7% greater ‘value’ in time – and it is what we produce in time that ultimately determines the value (NPV is after-all a measure of ‘$s in time’). This leads to the ‘cash flow grade’ concept, quite well described by Dr Brett King in a paper he write back in 1999. (I loved this paper when I first read it, as I was ~90% there myself - and felt frustration that Brett had written it first!) The concept also leads to the necessary identification of system bottlenecks that need to be exploited (#TheoryOfConstraints) to increase the ‘value flow’. These concepts are effectively what Gerald Whittle has effectively based his business on: the cash flow grade concept – combined with TOC – and using software to solve complex systems that can result. So – grade is just a proxy for value. Sometimes it can be a good proxy. Other times it can be a poor proxy. I have only ever once seen a feasibility study use a cash flow grade for the mine schedule. And I will be first to acknowledge it is not an easy thing to use. It can’t be directly measured like a grade. It must be calculated from multiple factors – of which grade is just one factor. And we usually often don’t have reliable models for those other relevant factors. My advice: do as much economic value modelling of your ore value as you can, and find some approach to cut-off value that is practical and makes sense, AND captures some of that time value. References King, B. 1999, “Cash Flow Grades - Scheduling Rocks with Different Throughput Characteristics”, Proc. Conf. Optimising with Whittle, Perth 1999.

Geotechnical focus: Core drilling and sampling standards are about controlling damage, not just drilling depth: correct core barrel and bit selection (HQ/NQ/T2), low-disturbance drilling parameters, full core recovery, proper orientation, and disciplined handling and boxing. Poor practice shows up immediately as low recovery, broken core, and unreliable RQD. Mineral exploration focus: In mineral exploration, core drilling standards aim to preserve geology and structure: appropriate barrel size, stable drilling parameters, accurate core orientation, and strict core handling and logging. Recovery quality directly affects structural interpretation, grade control, and confidence in resource models.

Comparto con ustedes este banco de preguntas que elaboré en compañía de mis colegas Diego Burgos Huanambal y Danny Cao. Esperamos que este material pueda ser de utilidad para su preparación académica y profesional en temas de minería. Sigamos aportando conocimiento y fortaleciendo nuestra comunidad minera.

Fundamental Pit Geometry ▪ Pit: The large, terraced excavation created to extract ore from near-surface deposits. ▪ Bench: A horizontal step cut into the pit, formed during mining to provide stability and access. ▪ Berm: A safety strip or horizontal shelf left between benches to catch falling rocks and support stability. ▪ Batter: The sloping surface of a bench wall. ▪ Batter Angle: The angle of inclination of the bench wall, measured from the horizontal. ▪ Face: The active rock surface where drilling, blasting, and loading occur. ▪ Crest: The top edge of a bench or slope. ▪ Toe: The bottom point where the bench slope meets the horizontal floor. ▪ Inter-Ramp Angle (IRA): The angle formed by a stack of benches without considering the berms at every level. ▪ Overall Angle: The final stable slope angle from pit crest to pit bottom, accounting for all benches and berms. Mining Operations & Equipment ▪ Pit Stages: Sequential mining phases that advance the pit outward and downward in planned steps. ▪ Loading: The process of picking up blasted rock using machinery such as shovels, excavators, or loaders. ▪ Hauling: Transporting ore or waste rock from the pit to dumps, crushers, or stockpiles using haul trucks. ▪ Haul Road: Engineered roads designed within the pit for the safe movement of heavy machinery. ▪ Shovel / Excavator: Primary loading equipment used to dig and load blasted rock. ▪ Front-End Loader: A versatile machine used for loading, stockpiling, and short-distance material movement. ▪ Dump: A designated area where waste rock is deposited. ▪ ROM (Run of Mine): Material delivered directly from the mine to the processing plant without any pre-crushing. ▪ Drilling: The creation of blast holes for explosives. ▪ Blasting: Fragmentation of rock using explosives to allow efficient excavation.

Last week I wrote about how mining companies are sitting on transformational value trapped in their data systems, and the comments that followed were fascinating. Most people agreed with the opportunity. But a recurring theme emerged: "That's all well and good, but what about the quality of the data in the first place?" Fair point. Because if you're going to build an AI-powered intelligence layer on top of decades of mining data, you'd better hope that data isn't complete rubbish. Recent industry surveys paint a confronting picture. The vast majority of mining professionals say data management is critically important to their organisation. Yet less than a third have an established framework for managing it. Most keep data "organised in various systems", which is corporate speak for scattered across a dozen folders, three legacy databases, and someone's USB stick. The historical data problem is particularly challenging. More than half the industry identifies unmanaged historical data as a significant challenge, yet only half feel confident their company can actually handle it properly. When you've got an average of 22 people touching datasets within an organisation and most companies can't reliably tell you who changed what, when, or why, you've got a recipe for expensive mistakes. In mining, decisions based on flawed geological data can lead to drilling in the wrong locations, overestimating reserves, or underestimating processing costs. Boards should be asking hard questions about this. So is fixing it actually possible? Yes. But it requires something the industry has historically resisted: discipline. The solution is in establishing clear data governance from the point of collection. Every piece of data needs provenance: who collected it, when, using what methodology, and what QA/QC processes were applied. The technology to automate most of this now exists. The barrier is cultural, not technical. Consider the alternative. You spend millions on an AI platform, hire a data science team, and build predictive maintenance models, only to discover your insights are based on assay results that were transcribed incorrectly in 2009. The billions sitting in server rooms that I mentioned last week? They're only accessible if the data is trustworthy. That’s why we aim to built our approach around data integrity from the start. AI technology must validate, cross-references, and flag anomalies before decisions are made. Because the most sophisticated algorithm in the world is worthless if it's trained on low quality data. Cleaning historical data and establishing proper frameworks isn't glamorous. It won't make headlines at mining conferences. But it's the foundation upon which every other digital transformation initiative depends.

It all began in the South African gold mines in the 1950s. An engineer named Danie Krige was frustrated by the inaccuracy of reserve estimates. The manual methods of the time were subjective, inconsistent and led to costly mis‑judgements. The problem was simple yet crucial: how could one estimate the grade of millions of tonnes of ore based on only a few hundred drill holes? 🔹 The solution came from France: Georges Matheron, a French mathematician, was struck by Krige’s empirical work. In 1962 he formalized the mathematical theory behind Krige’s observations, creating what we now know as kriging. The kriging revolution: Unbiased estimates with minimal variance Quantification of uncertainty through the kriging variance A rigorous statistical method replacing specialists’ “gut feeling” A mathematical foundation based on variograms and spatial correlation Transformational impact: Kriging was not merely an incremental improvement-it was a paradigm shift. For the first time in the history of mining, there was a method that not only estimated grades but also quantified the confidence placed in those estimates. 🔹 Concurrently, another revolution: In 1965, Helmut Lerchs and Ingo Grossmann solved another fundamental problem: what is the optimal pit? Their algorithm, based on graph theory, provided for the first time a mathematically optimal solution for delineating open pits. Before: manual pit design based on experience. After: mathematical optimization ensuring maximum economic value. The perfect marriage: Kriging provided reliable grade estimates. The Lerchs–Grossmann algorithm optimized economic recovery. Result: scientific planning in place of intuition. A historical note: the Lerchs–Grossmann algorithm only became widespread twenty years later, once computers were powerful enough. The Whittle 3D software of the 1980s was the milestone that democratized pit optimization. Why does this matter today? These two pillars-reliable estimation and mathematical optimization-laid the foundation of modern mining. Without them, we would not have the basis to integrate machine learning, stochastic simulation and advanced optimization, as we see today. The journey continues: 1960s: Kriging + Lerchs–Grossmann 1980s: Stochastic simulation 2000s: Optimization under uncertainty 2020s: Machine learning + geostatistics From South Africa to the world, from intuition to science, from deterministic to probabilistic methods.

Mine Manager’s Playbook Series Haul roads look simple. They are not. They are the biggest hidden cost centre in open-pit mining. A shovel can be world-class. A fleet can be brand new. Explosives can be perfect. But if the haul road is bad, the mine becomes slow, expensive, and unproductive — every single hour. The Financial Truth No One Talks About Every 1% increase in rolling resistance causes 10% loss in truck productivity. Just one soft patch, one wet curve, one ungraded segment… and your entire fleet behaves underpowered. You won’t see this in fuel sheets. You won’t see it in daily MIS. You’ll only see it in the total cycle time, the silent killer of mine economics. Where Haul Roads Drain Money 1️⃣ Fuel Burn Bad roads increase diesel consumption by 15–35%. Multiply that by 20–30 trucks and the numbers become brutal. 2️⃣ Payload Loss Operators start playing safe. “Almost full loads” replace full loads. You lose BCM quietly. 3️⃣ Tyre Life Crash Rough roads kill tyres 40–60% faster. Each tyre costs USD 3,000–5,000. A road can wipe out your tyre budget faster than any operator mistake. 4️⃣ Shovel Starvation Slow trucks → empty shovels → lost tonnes → lost month-end targets. 5️⃣ Maintenance Backlog More braking = more heat = more failures. Your workshop gets punished for road issues. The Core Science A Mine Manager must master one chain: Geometry → Rolling Resistance → Speed → Cycle Time → Cost/BCM → Profitability Fix this chain and your mine performance transforms. Four Engineering Truths • Geometry: Gradient, curvature, super-elevation. A truck runs on engineering, not hope. • Surface Quality: Corrugation, ruts, potholes — these are cost signals, not “road conditions.” • Width & Berms: If operators feel unsafe, they reduce speed. Safety and speed go together. • Drainage: Water destroys most haul roads. Poor drainage destroys the rest. The Behaviour Side (Most Ignored) Good road design loses 50% of value if behaviour is poor: – lane discipline – over-speeding on straights – shortcuts on curves – inconsistent water bowser cycles – reactive grading instead of scheduled grading Road science is half engineering, half discipline. The Most Important KPI in Open-Pit Mining Not fuel burn. Not tyre cost. Not tonnes per hour. It’s “Average Speed on Loaded Haul.” Increase it by even 1 km/h, and your mine’s monthly profit moves instantly. Mine Manager’s Non-Negotiables ✔ Grade every shift ✔ Maintain drainage religiously ✔ Enforce lane discipline ✔ Prevent truck queues at shovel ✔ Measure rolling resistance weekly ✔ Audit roads using drones ✔ Make road quality a production KPI Because the truth is simple: Mines rarely lose money in digging. They lose it on the road.

El verdadero seniority no se demuestra solo hablando: se demuestra antes, durante y después de la reunión. La mayoría de líderes creen que su presencia ejecutiva se construye solo “cuando hablan”. Pero la verdad es que el 70% de lo que proyectas ya estaba decidido antes de pronunciar la primera palabra. Lo veo todos los días. ▫️Gente que llega a reuniones críticas sin una intención clara. ▫️Profesionales senior que aún hablan desde la defensa y no desde la claridad. ▫️Líderes con experiencia que hacen preguntas “tácticas” cuando deberían formular preguntas que revelen visión. Y no es falta de conocimiento. Es falta de presencia estratégica. Porque una reunión de alto nivel no se improvisa. Se prepara como un atleta prepara una final: con intención, foco y energía emocional alineada. Cuando entras así, todo cambia: ✔️ Tu mensaje deja de ser disperso y se vuelve memorable. ✔️ Tus pausas proyectan seguridad, no dudas. ✔️ Tu lenguaje corporal respalda tu voz (no compite con ella). ✔️ Y al cerrar, no suenas “amable”: suenas senior. Y ahí está el secreto de los líderes que avanzan más rápido: No hablan más fuerte. Hablan mejor. Con propósito. Con límites. Con presencia. Piensa en tu última reunión importante: ¿Entraste con intención o reaccionaste a la situación? ¿Qué vas a cambiar en la próxima reunión?

1️⃣ Diamond Core Drilling (HQ/NQ/BQ) Produces intact core — the gold standard. If you want RQD, fractures, UCS, structure, or lab tests, this is your method. → Best for: Tunnels, dams, slopes, deep exploration. 2️⃣ Reverse Circulation (RC) Drilling Fast, efficient, and cost-effective. Delivers rock chips, not core. Brilliant for quick decisions in hard rock. → Best for: Reconnaissance, orebody delineation, grade control. 3️⃣ Down-The-Hole (DTH) Hammer A pneumatic hammer breaks very hard rock with ease. High penetration, low cost, no core. → Best for: Hard volcanics, blasting holes, geotechnical boreholes. 4️⃣ Top Hammer / Percussive Drilling (Jackleg, Stopper, Jumbo) High-frequency drilling where mobility matters most. If you’ve been underground, you’ve heard it before you saw it. → Best for: Underground headings, stopes, tunnel blasting. 5️⃣ Rotary Air Drilling (Tri-cone Bit) Fast and cheap. Produces cuttings only. Love it for dry, competent rock. → Best for: Recon drilling, pre-collar holes, shallow investigations. 6️⃣ Rotary Mud Drilling (in Fractured Rock) When the rock mass is weak, crushed, or sheared — mud keeps the hole open. Not ideal for core, but perfect for stability. → Best for: Shear zones, faulted rock, geotechnical instrument holes. 7️⃣ Horizontal / Directional Core Drilling When you need rock data in a specific direction — especially in tunnels or dams. Game-changer for proactive hazard detection. → Best for: Tunnel face probing, slope anchors, deep foundations. 8️⃣ Sonic Drilling (Moderately Hard Rock) Vibration-assisted advance that preserves core in weathered rock where diamond drilling struggles. → Best for: Rock-soil transitions, altered rock.

Key Takeaways Africa leads with 1,010 tonnes of gold, driven by Ghana, Mali, and South Africa. China (380t) and Russia (330t) are the world’s top national producers. Australia (284t) stands as the largest Western producer. Gold remains one of the world’s most valuable natural resources, central to everything from central bank reserves to jewelry and electronics. During 2025, the yellow metal set multiple price records, driven by a soft dollar, strong central bank buying and heightened global uncertainty. This visualization breaks down global gold production by region in 2024, spotlighting the top-producing countries and their contributions to the region supply landscape. The data for this visualization comes from the World Gold Council. Africa Leads Global Output Africa is the world’s top gold-producing region, generating 1,010 tonnes in 2023. Ghana leads the continent with 141 tonnes, followed by Mali (100 tonnes) and South Africa (99 tonnes). Region Production (tonnes) Largest Contributors Africa 1,010 Ghana (141), Mali (100), South Africa (99) Asia 665 China (380), Indonesia (140) CIS 584 Russia (330), Uzbekistan (129) North America 500 Canada (202), U.S. (158), Mexico (140) Central & South America 519 Peru (137), Brazil (84), Colombia (66) Oceania 346 Australia (284), Papua New Guinea (50) Europe 36 This dominance reflects the continent’s vast mineral resources, though political and operational challenges continue to affect output in some areas. China, Russia, and Australia Dominate Nationally China remains the top national producer with 380 tonnes, followed by Russia at 330 tonnes. These two countries alone account for almost 20% of global output. Australia follows with 284 tonnes, making it the leading Western gold producer and a cornerstone of Oceania’s 346-tonne total. The Americas Remain Strong Contributors North America produced 500 tonnes in 2023, driven by Canada (202 tonnes), the United States (158 tonnes), and Mexico (140 tonnes). Central and South America added 519 tonnes, led by Peru (137 tonnes), Brazil (84 tonnes), and Colombia (66 tonnes). Combined, the Americas contribute more than one-fifth of global supply. Source: https://www.visualcapitalist.com/charted-global-gold-production-by-region/

Hoy en día existe un análisis de tendencias, tecnologías e investigaciones enfocadas en transformar la industria minera hacia un modelo más inteligente y sostenible con un énfasis en el pilar de protección ambiental🌍. ⚒️ Minería 4.0 (Smart Mining) 👉 Uso de IoT, IA, Big Data, robótica, nube, gemelos digitales y ciberseguridad. 👉 Impacto en toda la cadena de valor: exploración, extracción y producción. 👉 Retos: comunicación en minas subterráneas y gobernanza de datos. 🌱 Minería 5.0 (Sostenible / Verde) 👉 Tecnología + Personas + Sostenibilidad. 👉 Uso de energías limpias (hidrógeno verde, solar, eólica). 👉 Meta de cero fatalidades con sistemas inteligentes. 👉 Minería con valor social y licencia social para comunidades. 👉 Economía circular, trazabilidad con blockchain y alineación a criterios ESG. 🌟 Casos reales: ✔️Anglo American (Sudáfrica) → camiones con hidrógeno verde. ✔️CODELCO (Chile) → realidad aumentada en entrenamiento. ✔️Mina San Cristóbal (Bolivia) → IoT para salud y seguridad. ✔️BHP (Australia) → Integración de energías renovables. ✔️Anglo American – Quellaveco (Perú) → Mina digital, CIO en Lima, 100% energía renovable, camiones autónomos. En tu opinión, ¿En el Perú, crees que las empresas mineras en su mayoría —sobre todo mediana y gran minería — ya tienen una tendencia clara hacia la Minería 4.0? Te leo 👍

Key Performance Indicators (KPIs) for Optimisation of Drilling & Blasting operation in Opencast Mines 1. Technical KPIs Monitor powder factor optimization Track fragmentation size distribution Measure blast-induced damage levels Assess drilling accuracy and precision Evaluate explosive utilization efficiency Monitor equipment productivity rates 2. Safety and Environmental KPIs Track safety incident rates Monitor vibration and noise compliance Assess air quality impact levels Measure flyrock occurrence frequency Document environmental compliance status Record community feedback and concerns Key performance indicators (KPIs) for optimization of drilling and blasting include * *Rate of Penetration (ROP) ** , Powder Factor, Rock Fragmentation , Equipment Utilization Cycle Time of Shovel Throughput of Crushers , Drilling and Blasting Costs, Ground Vibration, Bit Life, and Safety Incidents. These KPIs provide quantifiable measures to evaluate drilling effectiveness, blast performance, operational efficiency, and safety, facilitating continuous improvement and alignment with overall mine goals. Drilling Performance KPIs Rate of Penetration (ROP): Measures the speed at which the drilling unit can penetrate the rock. Penetration Rate (Depth per Hour): Assesses the productivity of the drilling unit over time. Bit Life: Tracks the durability and performance of drill bits. Equipment Utilization: Measures the effective working hours of drilling units against their total working hours. Drilling Costs per Foot: Evaluates the cost-effectiveness of drilling operations. Blasting Performance KPIs Powder Factor: The amount of explosive used per volume of rock blasted, used to optimize blast design. Rock Fragmentation: The size distribution of the blasted rock, a critical factor for downstream processes like crushing and milling. Flyrock: Measures the distance and potential danger of rocks thrown from the blast site. Back Break: Indicates the extent of rock damage beyond the intended blast perimeter. Air-Overpressure (AOp): Measures the force of the air blast, which can impact surrounding areas. Safety & Environmental KPIs Ground Vibration: Tracks the level of ground shaking caused by the blast, which is critical for maintaining structural integrity and minimizing environmental impact. Safety Incidents: The number of safety-related incidents during drilling and blasting operations. Overall Operational KPIs Cycle Time Optimization: Focuses on reducing the overall time taken for drilling and blasting cycles. Volume Blasted: Measures the total volume of rock successfully blasted within a given period. Energy and Environmental Metrics: Includes energy consumption and other environmental impacts to ensure sustainable operations.

Hydraulic Accumulators – The Hidden Power Behind System Reliability In every hydraulic system, there’s an unsung hero that ensures smooth performance, energy efficiency, and safety — the hydraulic accumulator. Whether it’s storing energy, absorbing shocks, or providing emergency power, accumulators play a crucial role in keeping systems stable and responsive. There are three main types: ⚙️ Weight-loaded – delivers constant pressure but is bulky. ⚙️ Spring-loaded – simple but limited by spring elasticity. ⚙️ Gas-charged (Piston, Bladder, Diaphragm) – the most widely used for their compactness, quick response, and efficiency. 💡 Applications go far beyond energy storage: • Safety systems in aircraft and railways ✈️🚆 • Energy saving in injection molding machines • Suspension and vibration damping in heavy vehicles 🚜 • Pressure stabilization in pumps and hydraulic circuits By using accumulators smartly, engineers can reduce pump load, save energy, and extend equipment life — all while maintaining system stability. 🔹 Small component, massive impact on system performance.

When we analyzed shift efficiency patterns for internal research, we discovered that many mining sites lose millions in productive time. This finding points to a structural problem: mining operations generate extensive operational data but lack systems to translate that data into actionable time utilization insights. Most managers track equipment hours and tonnage without understanding the relationship between these metrics and actual productive capacity. Our field studies revealed consistent measurement gaps across operations. A mine we researched reported 56-64% effective working time, with variance tied to blast and shift configurations. The real insight wasn't the efficiency range but rather the absence of systematic approaches to understand variance drivers. Time Utilisation Model (TUM) codes capture some activities while equipment resets and haulage queuing remain untracked. This selective visibility creates optimization bias: teams improve measured activities while unmeasured bottlenecks expand. The incentive structure analysis uncovered predictable but overlooked dynamics. Tonnage-based rewards drive short-term production at the expense of equipment utilization and maintenance windows. This creates cascading effects: increased wear rates lead to unplanned downtime, which compresses maintenance schedules, which further reduces equipment reliability. Standard shift designs assume static operating conditions, but actual mining environments require dynamic response capabilities. Supervisors receive performance data after shifts end, preventing real-time adjustments that could prevent minor delays from becoming production losses. The competitive analysis shows widening performance gaps between integrated and traditional planning approaches. Companies with modern planning systems capture 10-15% productivity gains through dynamic scheduling and bottleneck prediction. Legacy operators face both immediate cost disadvantages and reduced learning rates from limited operational feedback. In commodity markets where margins compress during downturns, operational efficiency differences determine which companies maintain positive cash flow. The shift planning problem isn't about time management but about building systems that convert operational data into sustained competitive advantage.

Today, key human resources indicators (KPIs) have become more sophisticated because they no longer only measure administrative efficiency, but also the strategic impact on the business. Here are 5 big blocks with examples: 1. Attraction and recruitment • Time to Fill: average days to fill a vacancy. • Time to Hire: from the time the candidate is contacted until they accept the offer. • Cost per hire: sum of investment in recruitment divided by the number of hires. • Quality of hiring: performance and permanence of the talent hired. 2. Performance and productivity • Productivity per employee: revenue or results generated / number of employees. • Compliance with performance objectives (individual OKRs/KPIs): % of goals achieved. • Absenteeism rate: absences vs. scheduled working hours. • Overtime worked: indicator of overload or poor planning. 3. Retention and Turnover • Voluntary and involuntary turnover rate: % of employees who leave the company. • Retention of key talent: permanence of critical profiles for the business. • Average tenure: average seniority of employees. • Turnover cost: expense associated with replacing an employee (recruitment + training + loss of productivity). 4. Development and training • Investment in training per employee: annual amount invested. • Training hours per capita: average number of training hours per employee. • Applicability index of the training: % of skills learned applied on the job. • Internal mobility: percentage of vacancies filled with internal talent. 5. Organizational climate and commitment • Employee Engagement Index: level of commitment and job satisfaction. • eNPS (Employee Net Promoter Score): willingness to recommend the company as a place to work. • Well-being index: perception of work-life balance, physical and mental health. • Level of diversity and inclusion: % of women, generations, multicultural profiles in key positions. The most current HR KPIs go beyond payroll and turnover: today they focus on employee experience, productivity, development, and strategic value for the business. At HRA Talent we can help you prepare an analysis with solution proposals so that your company is profitable and recovers an exceptional service culture!

Open pit mining is one of the most common and cost-effective methods used to extract valuable minerals from the earth. It’s like digging a giant bowl-shaped hole in the ground — layer by layer — to reach the desired ore. But behind this massive excavation lies the critical role of a Geotechnical Engineer — often working silently in the background to keep everything stable, safe, and efficient. 🧠 So, what exactly does a Geotechnical Engineer do in open pit mining? Let’s break it down with simple examples: 🪨 1. Slope Design & Stability Imagine cutting a big slice out of a cake. If the sides are too steep, it collapses. In mining, we design pit walls (slopes) that are stable enough to stand safely while also allowing the maximum amount of ore to be recovered. 📌 We study: Rock and soil strength Water pressure in the ground (pore pressure) Discontinuities (like fractures or faults) 🛠 Tools like limit equilibrium analysis, finite element modeling, and slope monitoring systems help us make decisions. 🌧️ 2. Water Control Water is a major enemy of open pit stability. 💧 Scenario: During heavy rain, water can seep into the pit walls, weaken the rock, and trigger landslides. ✅ Geotechnical Engineers design drainage systems, dewatering wells, and piezometers to manage groundwater and prevent failures. 🏗️ 3. Rock Mass Classification Every rock behaves differently under stress. We classify the rock mass using systems like: RMR (Rock Mass Rating) Q-System GSI (Geological Strength Index) 📌 This helps in selecting: Support systems (bolts, mesh) Slope angles Excavation methods 🚧 4. Monitoring & Risk Management We don’t stop after design — we monitor pit walls continuously. 📡 Using instruments like: Inclinometers Extensometers Prism monitoring with total stations Drone-based LiDAR and photogrammetry 📈 This allows us to detect movements early and warn the operations team before a slope failure occurs. 🛑 Real-Life Example: At a gold mine in a mountainous area, unexpected rainfall can cause slope instability. The geotechnical team may install piezometers and modify the pit slope angle. This timely intervention can prevent a major failure and save millions in lost ore and equipment. 💼 Whether it’s copper in Chile, gold in Ghana, or phosphate in Saudi Arabia — open pit mining cannot operate safely without geotechnical expertise. 👉 A small misjudgment in slope angle can result in catastrophic slope failure, risking lives, equipment, and production.

🫵 The plot Conservative practices contribute to substantial economic inefficiencies in the mining sector. Industry-wide impairment losses exceeded $120 billion during the 2013/14 commodity downturn, with resource and reserve estimation issues identified as contributing factors in 17% of feasibility study failures. McKinsey estimates $100 billion in potential value optimization across mining feasibility studies, with resource classification standards identified as one element requiring improved rigor. 🫵 The dirty little secret Ore deposit valuation hinges on robust reserve estimation, yet prevailing mining standards conflate geological confidence with grade uncertainty, leading to conservative biases and undervaluation. Geostatisticians often downgrade resources by focusing on local grade variability, ignoring geological reliability established in Indicated Resource classifications resulting in reduced reserves, deterring investment despite solid deposit foundations. The evaluation and valuation of ore deposits too often undermined by the conflation of geological confidence with grade uncertainty. 🫵 The gatekeepers labyrinth The classification of mineral resources and reserves has long struggled with this in both resource management and deposit valuation. Current practices, under the CRIRSCO umbrella, geostatisticians override established geological assurances based solely on grade variability, resulting in unnecessary reductions in reported ore reserves. Decoupling these risks and employing statistical confidence intervals, mining can preserve reserve estimates, capture upside potential, and enhance valuation accuracy. 🫵 Clear precedent The petroleum industry's Proven-Probable-Possible framework (PRMS), assigns explicit confidence intervals to reserve estimates without abandoning sound geological interpretations. The framework addresses both mineral resource classification requirements and deposit valuation imperatives, recognizing that classification decisions directly influence market valuations, access to capital, and investment returns. Such an approach recognizes that resource classification decisions directly impact deposit valuations, making the conversion of Mineral Resources Reserves relevant to the valuation of Mineral deposits. Ignoring this risk conflation attracts opportunity costs and consequential economic harm to investors because undervaluation of projects of well-understood deposits that receive inappropriate classification due to statistical limitations rather than geological uncertainty can be significant. Downgrading resource categories invokes different valuation multiples in market assessments resulting in poor market valuations that impede access to capital and capital formation. The misallocation of Mineral Resources can trigger the requirement for severe impairments charges that qualified persons seem aloof to.

Definition:It is the ratio of the volume or weight of overburden (OB) removed to the volume or weight of ore or coal extracted in a surface mine. SR=OB Removed (M³)/Ore or Coal Extracted (Tonnes) • A lower SR means more economical mining; a higher SR increases cost. 🔸Types of the SR: 1.Overall Stripping Ratio(OSR): It is the total quantity of OB removed divided by the total quantity of Ore/Coal mined during entire life of mine,Used for long-term planning. OSR=Total OB removed÷Total ore/coal mined during the mine’s life 2.Instantaneous or Bench SR (ISR or BSR): It is the ratio for a specific portion or bench of the mine,not the entire life,Used for short-term planning & assessing local variations in mine geometry. 3.Break-Even Stripping Ratio (BESR): It represents the max SR at which mining remains economically viable.Beyond this ratio,the cost of removing OB equals or exceeds the value of the ore (No Profit=No Loss). BESR=Value of Ore-Cost of Ore Mining/Cost of OB Removal 🔸Factors Affecting the SR: 1.Depth of Ore Body: Deeper deposits→ higher SR. 2.Thickness of Seam: Thicker seams→lower SR. 3.Dip of Seam: Steeper dips→higher SR. 4.Nature of OB: Hard/compact OB→increases SR cost. 5.Ore Body Shape & Continuity: Irregular or faulted ore→higher SR. 6.Mining Method & Equipment: Efficient,high-capacity machines→lower SR. 7.Haulage Distance: Longer haul→increases SR cost. 8. Market Price of Ore/Coal: Higher price allows higher SR to remain economic. 9.Cost of OB Removal: Higher removal cost→lower acceptable SR. 10.Groundwater & Drainage: Poor drainage→more OB handling→higher SR. 11.Mine Planning & Scheduling: Proper planning reduces SR;early stages often have higher SR. 🔸Need for Determining SR: Determining the SR is essential because it helps in: 1.Economic Feasibility: To know if mining is profitable or not. 2.Mine Planning & Design:Helps determine the Ultimate Pit Limit (UPL),pit depth & layout,selecting the most suitable mining method and equipment capacity. 3.Production Scheduling: Guides annual stripping and ore production targets.Helps maintain a balance between OB removal and ore extraction. 4.Cost Estimation and Budgeting: SR provides the basis for estimating OB handling costs,unit cost of production and preparing project budgets. 5.Environmental and Safety Compliance: Determines the volume of waste to be handled,dumbed & backfilled.Affects slope design,reclamation planning & stability analysis. 6.Decision on Cut-off Grade and Pit Limit: Helps establish the BESR & Final boundaries of the pit (i.e.UPL). 7. Equipment & Resource Planning: Used to select suitable machine & manpower requirements based on waste-to-ore ratio. •1.5:1 – Manual quarrying •2:1 – Semi-mechanized quarrying •3 to 4:1 – Bucket Wheel excavator •4 to 5:1 – Shovel-Dumper combination •8 to 10:1 – Dragline method The SR is a key economic indicator that determines the feasibility, profitability, mine design, pit depth & life of a mining project.

Personal Protective Equipment Saves Lives : Leadership and Safety in Action - SHE Part VI 🚨Every year, approximately 2.3 million women and men worldwide DIE from work-related accidents or illnesses a figure reported by the International Labour Organization (ILO). 🚨This tragically equates to more than 6,300 deaths every single day around the globe. 🚨The ILO further estimates there are over 374 million non-fatal work-related injuries annually, many of which could be prevented through consistent use of PPEs 🚨Studies also show that only about 64% of workers globally use PPE properly, highlighting a critical gap in safety culture and training, especially in developing regions. Personal Protective Equipment (PPE) is more than just a set of tools; it’s the frontline defense against workplace hazards. From construction to healthcare, PPE protects employees from physical, chemical, and biological risks, ensuring that everyone leaves work as healthy as they arrived. Essential Types & Proper Usage ⛑️ Head Protection: Helmets prevent head injuries from falling objects. 🥽 Eye and Face Protection: Safety glasses and face shields block hazardous particles and chemicals. 🎧 Hearing Protection: Earplugs protect against damaging noise. 😷 Respiratory Protection: Masks and respirators filter out harmful substances. 🧤 Hand Protection: Gloves safeguard against cuts, burns, and exposure. 🥾 Foot Protection: Safety boots reduce risks of impact or punctures 👷‍♂️ Body Protection: Work clothes for body parts and vests protect body parts 🎽 Fall Arrest: Protective vests, belts and hooks protects fall injuries because of working in height Why PPE Matters ✅ Minimizes exposure to hazards, preventing serious workplace injuries and occupational illnesses ✅ Safeguards workers from long-term health risks, such as chemical exposure or hearing damage ✅ Supports business continuity by reducing absenteeism, medical costs, and downtime from accidents Always ensure PPE is well-fitted, undamaged, and used as instructed. Regular usage and awareness Trainings, Safety Walks, Checks and Behavioral Observation Programs are key! Benefits Beyond Safety 👍 Boosts employee confidence and morale, knowing their wellbeing is prioritized 👍 Meets legal and regulatory requirements, avoiding fines and legal complications 👍 Demonstrates a proactive safety culture that values every worker. By prioritizing PPE, companies build a foundation for operational excellence and show real leadership in workplace safety.

Strength of materials - 20 Structural Analysis - 16 RCC-36 Soil & Foundations - 74 Hydrology - 20 Fluid Mechanics - 50 Irrigation - 50 Environmental Engineering - 50 Transportation - 77 It will be very helpful for every competitive Exams like ESE, GATE, SSC, DDA,BARC, ISRO, PSUs and State service Exams etc.

Too often, Reserves reporting is misunderstood—especially by non-technical stakeholders—as a self-contained exercise. In reality, reporting Mineral Reserves in accordance with the JORC Code (2012) or KCMI is not an objective in itself. It is the formal summary of a Life-of-Mine plan that has already been developed to a sufficiently detailed level. As per JORC Clause 29: "A Mineral Reserve is... defined by studies at Pre-Feasibility or Feasibility level as appropriate that include application of Modifying Factors." In operating mines, this study is typically the Budget-level Life-of-Mine Plan—aligned with AACE Class 2 estimates under Recommended Practice 47R-11, a framework also recognised by CRIRSCO. 📌 Key Point: A Reserve cannot be declared without a supporting mine plan. Attempting to do so is not only non-compliant, it risks misleading stakeholders. ✅ The Reserves statement is the output, not the input or objective, of mine planning.

Me permití hacer una investigación acerca de la Permisología Minera en los principales países del Mundo (incluyendo varios de Sudamérica), la información es relevante para nosotros en el Sur que necesitamos de reformas políticas sólidas para incrementar el atractivo de empresas basado en la confianza para invertir y por ende de generar desarrollo para el país y para la población: Página 1: Marcos regulatorios y permisos mineros en Sudamérica Página 31: Principales jurisdicciones mineras y marcos regulatorios (Europa, Norteamérica, Oceanía, África) SECCIÓN I: PERMISOS DE EXPLORACIÓN Página 50: Cuadro Permisos de Exploración (Europa - Norteamérica) Página 51: Cuadro Permisos de Exploración (Sudamérica) Página 52: Cuadro Permisos de Exploración (África y Oceanía) SECCIÓN II: PERMISOS DE DESARROLLO Y EXPLOTACIÓN Página 53: Tiempos totales y permisos requeridos: Europa y Norteamérica Página 54: Tiempos totales y permisos requeridos: Sudamérica Página 55: Tiempos totales y permisos requeridos: África y Oceanía SECCIÓN III: ANALISIS COMPARATIVO Y CLASIFICACIONES Página 56: Clasificación global por eficiencia (Tiempo total desarrollo) Página 56: Factores críticos de Éxito y Fracaso SECCIÓN IV: TENDENCIAS Y REFORMAS 2024-2025 Página 57: Principales reformas en curso

Mine planning is the heart of any successful mining operation — it transforms geological resources into valuable, mineable reserves, ensuring technical, economic, and operational feasibility. 📊 Here's a simplified look at the Mine Planning Cycle — from resource modeling to operations: 🔹 Geological Model: The foundation — understanding the ore body. 🔹 Optimization Inputs: Mining method, geotechnical data, costs, and constraints. 🔹 life of Mine (LoM) Considerations: Business goals, community impact, and operational limits. 🔹 Optimization & Its Results: Selecting the best pit shell for maximum value. 🔹 Mine Design: Creating the final pit, haul roads, and infrastructure layout. 🔹 Production Schedule: From LoM to weekly plans, aligned with NPV optimization. 🔹 Financial Modeling: Forecasting cost, revenue, and profitability. 🔹 Reconciliation: Comparing plan vs. actual — adjusting for real-world conditions. 🔹 Operational Plan: Turning plans into safe and efficient daily mining activities. 💡 This cycle is not linear — it’s dynamic and requires constant adjustment based on field feedback, economics, and safety. As a mining engineer, mastering this cycle means improving both productivity and sustainability.

I've been thinking about why some mining companies successfully adopt innovation while others struggle, despite having similar resources and challenges. The difference often comes down to organizational readiness. It is not a question of company size, but it is always a question of culture and legacy systems. As part of our business model, Objectivity performs a scoping study to demonstrate how DRX can improve drilling efficiency (defined as expected volume meeting QP/CP requirements per metre drilled). When compared to a baseline plan, DRX will improve the efficiency and identify other value drivers leading to better drilling outcomes. Through this process, I’ve observed some clear indicators that distinguish companies genuinely open to innovation and change. 1. Decision-making speed. If it takes you longer to get your new “innovative” supplier onboarded and under contract than it does for the innovator to deliver or demonstrate their value proposition, you're probably not ready. We often confuse thoroughness with delay. If your approval process for testing new approaches involves multiple committees and months of deliberation, you're structurally designed to resist innovation. 2. Onboarding process for new technologies. Do you have a clear pathway from concept to implementation? Or does each new idea get treated as a unique event requiring special handling? Innovation requires a process for rapid experimentation and learning, not perfect first attempts. 3. Systems to assess whether an innovation was effective? I've seen companies implement new technologies without clear metrics for success. They can't tell you whether the innovation worked because they never defined what "worked" means. Often, there is no baseline performance data to compare to, or, there is plenty of data, but little metadata describing the data or its quality. 4. Collaboration across disciplines The most successful projects we have had are the ones where everyone involved benefits from working directly with people who know their domain inside out. Resource drilling eventually has to turn into a reserve, so you might as well get the engineers on the call. And if you’re already running a producing mine, you could get the production department involved. It may help them understand why they should support the rigs. 5. Innovation/adoption is principality driven by culture. Good culture equals good and happy people equals good corporate growth (think Costco). A rigid hierarchy and lack of responsibility stops innovation. You need an environment where technical personnel, domain experts, and decision-makers can talk openly, where responsibility and trust flow freely, not where they're trapped behind layers of legacy process. Build systems based on respect, speed, fairness, clear measurement, and genuine openness. Flatten your structure, empower your people, and watch efficiency grow. Or don’t.

En minería, solemos escuchar frases como “hay que optimizar el proceso” o “necesitamos mejorar la operación” y para algunos resulta ser lo mismo, aunque parecen equivalentes, no lo son. La optimización y la mejora continua persiguen metas distintas, se aplican en momentos diferentes y exigen herramientas propias. Técnicamente, "Optimización" se define como la búsqueda del mejor resultado posible a través de un estudio matemático y simulación. Dicho en otras palabras optimizar significa ajustar un proceso para lograr el máximo rendimiento o el mínimo costo dentro de ciertas restricciones. En minería a cielo abierto, un ejemplo típico es la programación de fases con softwares como Whittle o Vulcan, que buscan maximizar el valor presente neto del yacimiento. En ese mismo camino, en minería subterránea, la optimización se refleja en metodologías como Mine-to-Mill, que ajustan desde la fragmentación en tronadura hasta la molienda, reduciendo consumo energético y elevando la recuperación de mineral. Mientras que la Mejora continua se define como pequeños pasos que construyen resultados. Es decir la mejora continua se centra en identificar fallas, eliminar ineficiencias y reforzar la cultura de trabajo. Aquí entran metodologías como el ciclo PHVA (Planificar, Hacer, Verificar, Actuar). En la práctica, puede significar ajustar protocolos de ventilación en minería subterránea, reducir la dilución en perforación y tronadura, o rediseñar el flujo de transporte de mineral en faenas a cielo abierto. Estos dos conceptos podrian explicarse facilmente de la siguiente manera. - Optimización: es cuantitativa, se apoya en modelos y simulaciones. - Mejora continua: es iterativa, sistematica, puede llegar a ser repetitiva se enfoca en hábitos, cultura y ajustes prácticos. - Secuencia lógica: mejorar primero, optimizar después. ¿Por qué importa esta distinción? Un proceso mal diseñado no puede ser optimizado con éxito. Primero hay que mejorarlo, estandarizarlo y estabilizarlo. Solo después es posible aplicar herramientas de optimización que realmente generen valor. En minería, confundir estos términos puede llevar a inversiones en software avanzado sin haber resuelto antes problemas básicos de gestión, seguridad o cultura organizacional. Reflexión final Optimización y mejora continua no compiten: se complementan. La primera ofrece resultados medibles y cuantificables; la segunda asegura que esos resultados se sostengan en el tiempo. Juntas, son la clave para una minería más rentable, eficiente y sostenible.

Earlier, I published an article detailing how I did some vibe coding to build a Python-based implementation of a pit optimiser using the Pseudoflow algorithm. While it is a powerful tool well-suited to this task, it has a significant limitation: it is not open source. According to its license, it may be used for educational, research, and not-for-profit purposes without a signed licensing agreement. However, anyone wishing to use it for commercial applications must contact the original developer to obtain a commercial license. This limitation creates a real obstacle for practitioners and developers who want to integrate pit optimisation into open‑source mining tools, commercial software, or industrial workflows without running into legal or financial hurdles. And honestly, it doesn’t work for me either. Imagine wanting to start your own mining consultancy but too broke to buy commercial mining software. Even if you build your own tool, you can’t legally use it to make a profit because of licensing restrictions — you’re basically doomed before you even start. But for every wall that stands in your way, there’s always a way through. And that’s exactly where open source changes the game. It levels the playing field — you don’t need deep pockets, just skill, time, and the determination to build something that works. And just like the Bear Grylls meme... That’s why I’ve decided to shift gears and focus on building an open‑source alternative to the Ultimate Pit Limit (UPL) optimiser — free, transparent, and accessible to anyone, whether for research or for business. And this is just the starting line. Pit optimisation is only the first step toward a bigger vision of open‑source mining software — a future where tools aren’t locked behind paywalls or buried in restrictive licenses. Pseudoflow is powerful, no doubt. But it doesn’t fit that vision. Open source does. Exploring Open Source Alternatives Looking for an open‑source alternative to pseudoflow for pit optimisation, I asked my "LLM advisor" for some pointers and ended up with two python graph libraries that can handle max‑flow/min‑cut algorithms — the core of pit optimisation logic. These are iGraph and PyMaxflow. Both are fully open source and widely used in the graph theory and computer vision communities, respectively. 1. igraph igraph is a general-purpose graph library available in Python, R, and C, widely used for network analysis and graph theory tasks. Interestingly, it also includes a maximum flow solver, which I initially overlooked. My original pit optimiser used igraph solely for graph construction, while the flow computation was handled by the external Pseudoflow library. Only later did I realise (thanks to Chat GPT) that igraph itself can solve max-flow problems. igraph is released under the GNU General Public License (GPL), making it fully open source and suitable for both academic and commercial use — as long as GPL license terms are respected. Algorithm Used igraph implements the Push-Relabel algorithm (also known as the Goldberg-Tarjan algorithm), a well-known method for solving the max-flow problem. This algorithm is efficient and generally performs well for large and dense graphs, though it may not be as fast as some specialised implementations for certain sparse or structured inputs, like mining block models. I believe that some commercial pit optimisation software also implements Push-Relabel (or variants of it), likely due to its balance between theoretical guarantees and practical efficiency. This further supports the idea that igraph, while not originally designed for mining, can serve as a viable backbone for prototyping or even powering lightweight open-source optimisers. Usage You can find the repo for the pit optimiser using igraph here: https://github.com/m-r-v-n/pit-opt-igraph Sample block model file used for the optimisation: https://github.com/m-r-v-n/pit-opt-igraph/blob/main/marvin_copper_final.csv Usage remains largely the same as in the original optimiser. The key difference is that it no longer requires explicit search boundary parameters for the X and Y axes. Instead, the spatial search area is now automatically calculated based on the num_blocks_above parameter. This makes the setup simpler and more intuitive, while still maintaining control over the vertical extent for the slope calculation 2. PyMaxflow PyMaxflow is a Python wrapper around a C++ implementation of the Boykov–Kolmogorov (BK) max-flow/min-cut algorithm, originally developed for image segmentation in computer vision — a problem that’s structurally quite similar to pit optimisation. Given its performance characteristics and specialisation, PyMaxflow is a strong candidate for building a robust, open-source Pit Optimiser. The BK algorithm isn’t always the fastest in theory, but it performs exceptionally well in practice on many sparse, grid-like graphs — which closely resemble mining block models. Like igraph, PyMaxflow is released under the GNU GPL license, making it fully open source and freely usable in both academic and commercial settings — as long as you comply with GPL terms. Usage You can find the repo for the pit optimiser using PyMaxflow here: https://github.com/m-r-v-n/pit-opt-pmf Sample block model file used for the optimisation: https://github.com/m-r-v-n/pit-opt-pmf/blob/main/marvin_pmf.csv Just like the igraph-based Pit Optimiser, usage is nearly identical to the previous Pseudoflow Pit Optimiser - the only required parameter for the search boundary calculation is num_blocks_above. Also, for the Pymaxflow input data, the index column in the block model file must start at 0 (zero-based indexing). If it starts at 1 or any other value, the optimiser will raise an error during graph construction or execution. Be sure to check and adjust your input data to prevent issues. Optimisation Result Below you can see the optimisation result from the Pseudoflow, igraph, and PyMaxflow. The Pseudoflow result is the one used from the previous article while the igraph and PyMaxflow result were made at a later time. All 3 were done in a free tier Google Colab All three implementations produced the same undiscounted cashflow, but the real differentiator was optimisation time: Pseudoflow: 31.76 seconds igraph (Push–Relabel): 2.19 seconds PyMaxflow (BK): 0.77 seconds Just a quick heads-up: all of these tests were run in a Python environment, so results may vary if you try them in a different setup like C++. Performance will also vary depending on the machine — for example, when I ran PyMaxflow multiple times in Colab, the optimisation time ranged anywhere from 0.3 s to 1 s. That said, even with that variability, PyMaxflow’s performance really stood out — and as a miner, I have to dig deeper (pun intended). A quick search online led me to a 2020 academic article exploring the use of the BK algorithm for ultimate pit limit optimisation — clear evidence that the algorithm is applicable beyond its original field. The PyMaxflow library has been publicly available in PyPi since 2014. This library is based on the 2004 version of the BK algorithm as described in: "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision," by Yuri Boykov and Vladimir Kolmogorov, published in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), September 2004. So yeah, the BK algorithm has been around since 2004 — but did no one in mining (including me) actually notice? Or were we all just too busy mining our business? (Another pun intended.) Anyway, while the optimisation itself is impressively fast in Python, the real bottleneck isn’t the solver — it’s the graph construction, particularly the generation of precedence arcs. This step is both computationally intensive and memory-hungry, often accounting for the bulk of the total runtime. With tens or even hundreds of millions of arcs, it can quickly overwhelm your system’s RAM, making it difficult to run on a standard machine without encountering performance drops or crashes. This is especially true with my implementation, which includes support for variable slope angles — a feature that adds even more complexity to the arc-generation logic. If you find ways to optimise or improve it, I’d genuinely love to hear about it — feel free to reach out and share your improvements! PyMaxflow Limits I did some stress testing in Google Colab using the 300 GB RAM backend, and frustratingly, the process crashed once the number of generated arcs hit around 1 billion. I spent quite a bit of time assuming the issue was in my code, only to eventually uncover the real culprit: a 32-bit limitation that caps the number of arcs at 2³⁰ - 1. No matter how much memory you have, once you hit that threshold — it's game over. Now, theoretically, it might be possible to lift that cap by tweaking the library to support 64-bit indexing. Whether that’s practical or advisable… well, let’s just say there could be ways. But for most users, it’s probably better to manage arc count conservatively and stay well below the limit. Wrapping Up And I guess that’s it — for now. What started as a search for an open-source alternative quickly evolved into a deep dive into optimisation speed, algorithmic trade-offs, and performance tuning. From Pseudoflow to Push–Relabel to Boykov–Kolmogorov, it’s clear there’s more than one way to optimise a pit — and some are faster, lighter, and freer than others. But don’t think of the tools I’ve shared here as a finished product. Think of them like a car with stock parts — functional, (un)reliable, and ready to go. With the right tuning, upgrades, and creativity, you can turn it into a 10-second car. There’s still plenty of room for improvement, especially around memory usage and arc generation performance. If you find ways to optimise or extend it, I’d genuinely love to hear about it. What’s Next? Pit optimisation is only one piece of the mine planning puzzle. The next big step? Long-term scheduling — and that’s exactly what I’m diving into now. I’ve been digging into optimisation techniques for block sequencing beyond classic MIP — things like simulated annealing, tabu search, large neighborhood search, hybrid methods, and maybe even a bit of reinforcement learning. It’s a tougher challenge, but definitely an exciting one! And yes, it will be open source. Stay tuned — I’ll be sharing progress and updates soon! Until then, happy optimising!

A Practical Reflection on Hidden Costs, Precision Engineering, and Operational Survival This week, I revisited a robust technical report on operational costs in mining. The document, published by the respected SRK Consulting, is a valuable compendium: it details costs by activity, separates fixed and variable expenses, and discusses methodologies such as Activity-Based Costing (ABC), among other essential practices for reducing expenses and maximizing value. However, one thing caught my attention: there was no mention of the cost of error — the kind that doesn’t appear directly on spreadsheets, but is paid for through rework, metallurgical losses, poorly executed blasts, geotechnical instability, or even irreversible social and environmental impacts. I couldn’t help but recall 2019, when I was in Chile for a summer internship, visiting a low-grade iron ore operation. In that challenging context, margins were so tight that attention to every detail made a difference, and all quality and uncertainty control systems (QA/QC) were tuned to the highest level. I witnessed precision engineering being used as a tool for economic survival. In that setting, error simply wasn’t an option. At that moment, I realized a stark contrast with the mindset I’ve often seen in Brazil: the idea that "errors can be absorbed", a mentality historically supported by high ore grades and a more forgiving market environment. But that reality no longer exists. Today, mining operations in Brazil are subject to much stricter demands for precision, control, social responsibility, and sustainability. So, the big questions are: 👉 How much does an error really cost? 👉 What is the impact of uncertainty on our decisions? 👉 Why is there still resistance to modeling uncertainty as a strategic cost? As my professor Joao Felipe Costa wisely says: “Error exists, and our role is to quantify the space of uncertainty.” And he’s absolutely right. There are now statistical models, geotechnical systems, sensors, simulations, and algorithms that allow us to measure operational risks and uncertainties with remarkable precision. And every percentage point of ignored uncertainty is, in practice, a hidden cost that undermines the competitiveness and sustainability of any operation. In a sector under pressure from narrow margins and high responsibility, increasing environmental pressure, stricter social demands, and a diversity of political and economic conditions, incorporating the cost of error into decision-making models is no longer a luxury: it’s a technical and ethical urgency. Ignoring the cost of uncertainty is no longer acceptable. It’s time to turn this “invisible Cost” into a strategic indicator. Claiming certainty without knowing the degree of uncertainty doesn’t eliminate the error. The error exists — and must be accounted for. Precision Engineering Is Impact Engineering We are called to act with excellence and responsibility. We must look beyond the visible CAPEX and OPEX, and include the variables that truly define long-term viability: error, uncertainty, trust in data, and a commitment to social and environmental impact. That is the kind of engineering I believe in. That is the future I want to help build. If you believe in this too, let’s talk. 📩 Leave a comment or send me a message. 💬 Let’s turn data into decisions — together.

Graphite is one of the most versatile and misunderstood materials in the modern industrial world. A crystalline form of carbon, it consists of stacked layers of graphene — a single sheet of carbon atoms arranged in a hexagonal lattice. Graphene is one of the best conductors of heat and electricity known to science, and in graphite form, it delivers a rare combination of exceptional conductivity, high thermal stability, chemical inertness, and remarkable strength-to-weight ratio. With a melting point of 3,650°C, superb lubricity, and resistance to corrosion, graphite is indispensable across sectors — from energy storage to high-temperature manufacturing. But to understand graphite’s future, it’s essential to separate myth from reality, especially in the battery market. Two Graphites, Two Stories There are two main categories of natural graphite: amorphous and flake. Amorphous graphite is a microcrystalline form derived from needle coke, a byproduct of fossil fuels like coal or tar. Fine and powdery, it’s typically low in purity and requires significant refining for high-tech use. Although natural in origin, its heavy processing means it’s often labelled “synthetic” — partly to distinguish it from flake graphite. Flake graphite is the crystalline variety, formed in metamorphic rock. Found in flat, plate-like particles with angular edges, it’s made up of stacked graphene layers. Flake size matters: jumbo and large flakes can sell for 3–5× the price of fine flakes. In 2024, medium and large flake prices rose by ~10%, while fine flake prices dropped by ~20%. The natural flake market is expected to grow at a CAGR of 14.6% from 2025–2032, driven by high-performance applications. The Big Advantage: Expandability Large flake graphite can be converted into expandable graphite — a form that expands up to 100–300× its original volume when heated. Expanded graphite is then compressed into graphitic foils, prized for: Thermal conductivity (up to 500 W/m·K in-plane) Electrical conductivity Chemical resistance Durability in extreme environments Graphitic foils excel in electronics, acting as heat spreaders, thermal interface materials, and heat sinks in smartphones, tablets, LEDs, laptops, and servers. With the rise of compact, high-power devices, their ability to dissipate heat is critical to preventing performance throttling and extending lifespan. Traditional and Emerging Uses Graphite’s industrial role extends well beyond batteries: Steelmaking & Refractories – Furnace linings, crucibles, and molds, plus carbon addition in steel production. Lubricants & Brake Linings – Dry lubrication in high-friction environments such as automotive brakes. High-Tech Composites – Graphene production for electronics, fuel cells, solar cells, and plastics with enhanced thermal and mechanical performance. The Battery Market Reality Despite popular belief, most battery anodes are not made from natural graphite alone. In 2024, around 90% of battery anode graphite was synthetic and only 10% natural — a dramatic reversal from a decade ago, when natural graphite made up 60–65%. Why the shift? Synthetic graphite offers: Exceptional purity and consistency Longer cycle life and better SEI (solid electrolyte interphase) stability Superior performance for fast charging Better electrolyte compatibility Synthetic costs roughly 2× more than natural, but for electric vehicles, the longevity advantage often outweighs the price premium. Natural Graphite’s Comeback in Blends Natural graphite still has a strong case: Higher specific capacity (~372 mAh/g theoretical) Lower cost Better energy density after processing (e.g., spheronisation, coating) For this reason, many EV makers are now using blends — ~10% natural in EV batteries and up to 30% in stationary energy storage systems. This delivers a balance of performance and cost. Demand Outlook Global lithium-ion battery demand is growing at 12–20% CAGR, and that’s good news for both graphite markets: 3–4× growth in total graphite demand 6–8× potential growth for natural graphite if its share in anodes doubles With ~40% of current natural graphite output (~700,000 tonnes/year) already going into batteries, and additional demand for high value large flake graphite in electronics and high-performance applications, the future for mined graphite looks bright — even if synthetic dominates battery anodes. Bottom line: Graphite isn’t just “the battery mineral.” It’s a foundation of many industries, and is increasingly becoming a cornerstone of thermal management in electronics, and a critical material for the energy transition. The key to its future lies in innovation, advanced coatings and blending natural and synthetic for optimal battery performance, scaling production for new technologies, and expanding its role in the high-tech supply chain.

En los estudios económicos de un proyecto minero, las reservas juegan un rol crítico en la valorización del mismo. En el gráfico, se muestra cómo una variación del 1% en las reservas minerales (por tonelaje o ley) puede modificar el NPV en aproximadamente $6.2 millones de dólares. Esta relación proporcional evidencia la alta sensibilidad del NPV frente a las reservas. En este ejemplo se tiene un NPV base de 288 millones de USD. Una disminución del 30% en las reservas podría reducir el NPV a 105, mientras que un aumento del 30% lo elevaría hasta 474 millones. 📊 Fuente: Mining Project Value Optimization

Lesson 7 – Part 3 Sometimes, your explosive train doesn’t misfire completely — It starts, but somewhere along the hole, the detonation wave dies out. This is a Cut-Off, and it’s just as dangerous. ⸻ 🔍 What causes a Cut-Off? – Poor continuity in the explosive column – Gaps between boosters or cartridges – Water degradation of explosives – Bad priming (wrong type or position) – Column collapse due to improper stemming or geological failure ⸻ ⚠️ Why is it dangerous? – You think the hole fired, but part of it didn’t – There could be unexploded explosive deep in the hole – Secondary blasting becomes extremely risky – Drill rigs hitting unexploded sections can cause fatal accidents – You lose performance: poor fragmentation and high oversize ⸻ 🛡️ How to prevent Cut-Offs: ✅ Use continuous explosive column — no gaps ✅ Ensure water-resistant explosives in wet holes ✅ Double-check booster placement and priming ✅ Proper stemming with suitable material and technique ✅ Avoid drilling in soft zones that collapse during loading ⸻ 🛠️ Field Practice: – Log all holes and match explosive types to hole conditions – After blast, inspect muckpile signs — look for signs of poor breakage – If a hole is suspicious, treat it like a misfire until confirmed otherwise ⸻ 🎯 Remember: A Cut-Off is a misfire in disguise. Just because it started doesn’t mean it finished. Treat it with the same caution — and plan to avoid it from the start.

Cutoff, production rate, value - shift one, shift them all. How do you strike the right balance? A sensitivity analysis is commonplace in any technical report - assessing project value when a single variable changes in isolation. The problem is that a single variable seldom if ever changes in isolation. Every decision has a ripple effect. You can't optimize one variable without impacting the others. Yet many project charters etch a singular focus from the start- whether it’s minimizing capital intensity, maximizing throughput, or squeezing cost per tonne. As a result, studies tend fixate on technical hurdles, while overlooking business fundamentals and project delivery drivers. I'm not throwing shade at the importance of technical rigor. But a technically sound project that never gets built is just a good-looking spreadsheet. So how do we widen our focus? Start by modeling mining's three-body problem - cutoff, production rate, and value. The concept is simple: > Model scenarios using different cutoffs, methods, production rates > Estimate resulting NPVs > Identify where the value peaks It's powerful at concept, scoping or PFS, where the range of outcomes is still wide enough to test. For project teams, it exposes trade offs that aren't obvious in isolation. But the value of this method hinges on your assumptions. Cost curves, recovery factors, geotechnical constraints, commodity pricing. Get these wrong and your "value" is nothing but a mirage. Still, when grounded in good data and sound judgement, it can change the trajectory of a project. It moves the conversation from: “What can we do with this deposit?” to “What should we do to maximize its value?” It's not just a technical exercise. It's a strategy tool, best applied before your options narrow. The insights are worth the effort, but remember... Garbage in still means garbage out. Have you tried balancing mining's three-body problem? How did it influence your design or strategy?

📌 Post 4: EDA- Exploring the Data Before Modeling In geology, this is called EDA (Exploratory Data Analysis): the process of visualizing, understanding, and detecting patterns or errors in your data before modeling or estimation. 🔍 What does EDA look for in geology? ✅ Grade distribution – Are the values normal, skewed, or multimodal? ➡️ Histograms, boxplots, correlation matrix. ✅ Outliers – Is it an error or a geological anomaly? ➡️ Boxplots, scatter plots ✅ Gaps or unsampled intervals – Is there continuity in the data or zones without information? ➡️ Depth charts, heatmaps ✅ Behavior by geological unit – Do grades change by lithology? ➡️ Boxplots by geological unit ✅ Grades by depth or zone – Are there vertical or spatial trends? ➡️ Scatter plots, grade vs. depth profiles ✅ Comparison between campaigns or laboratories – Are there systematic differences? ➡️ Boxplots by group, comparative scatter plots 📊 A good EDA helps answer key questions: 📌 Where are the rich and poor zones? 📌 How does mineralization vary by rock type? 📌 Is there bias between methods or campaigns? 📌 How continuous is the sampling? 💡 Remember: you can’t estimate what you don’t understand. And EDA is also part of QA/QC, because it helps detect systematic errors or inconsistencies that you wouldn’t notice through geological validation alone. 🎯 In summary: ➡️ EDA is not just about visualization… ➡️ It’s about interpreting, questioning, and preparing your data for estimation with geological criteria. 💬 What charts do you use in your EDA? Have you ever been surprised? Note: Image for illustrative purposes only 😉

Dans l’estimation et l’exploitation des blocs miniers, certaines erreurs peuvent coûter cher. En voici quelques-unes que j’ai pu observer ou éviter : 1️⃣ Mauvais choix de dimensions de bloc : des blocs trop grands masquent les variations de teneur, ceux trop petits surchargent les calculs. 2️⃣ Ignorer la dilution : négliger la dilution géologique ou opérationnelle fausse la rentabilité réelle. 3️⃣ Sous-estimer les pertes minières : oublier les zones inaccessibles ou les chantiers non récupérables biaise le modèle. 4️⃣ Utiliser une densité générique : chaque lithologie a sa propre densité ; la précision est essentielle pour un tonnage fiable. 5️⃣ Mal définir le seuil de coupure (cut-off) : un cut-off mal calculé conduit à l’exploitation de blocs non rentables… ou au rejet de blocs riches. 6️⃣ Absence de validation terrain : sans recouper le modèle avec les données de production, on reste dans la théorie. 📌 Une bonne analyse géométrique repose sur des données solides, des hypothèses réalistes et une collaboration étroite entre géologues, ingénieurs et métallurgistes.

Open-pit mine planning plays a crucial role in defining mineral reserves and optimizing their extraction. The extraction process typically involves mining sequential pits known as pushbacks, which must satisfy complex geometrical constraints to ensure operational efficiency and safe equipment usage. However, current pushback designs are largely manual, rely-ing heavily on the expertise of engineers to translate outputs from commercial tools into practical solutions. This article introduces a novel algorithm inspired by the physical principles of soap bubbles, leveraging their natural tendency to form compact, efficient shapes. The algorithm integrates a new mathematical formulation that simultaneously considers both the economic value and geometric characteristics of pushbacks, producing compact and operationally feasible clusters of blocks. Extensive testing on real mines with large datasets demonstrates the algorithm’s capability to generate practical pushbacks that meet both economic and operational requirements. Source: https://link.springer.com/epdf/10.1007/s42461-025-01191-7?sharing_token=zpVXSVTE85R1u0v8UVOXave4RwlQNchNByi7wbcMAY6f6iIrkDcZJvLhwsUl34VXMfbSgscTxjsiHZlo0kRQNblp0x5fAdQlUpmaYS629fa1_C7gU3LzFZP-_slBV5Jr_3WbUW_i7rvRWSYThnPpV4VlzkODcv9nn2kzScb7H1c%3D

Reverse Circulation (RC) Drilling Rig: is a widely used technique and quick drilling method used in mineral exploration and method of testing the size, grade, and geology of mineral deposits before mining starts, where compressed air is used to push rock cuttings through the inner tubes of drill rods to the surface. This allows for the collection of fine rock samples for analysis and providing geological information at regular intervals . RC Rig Functions: Drilling: Dual-tube drill rods (inner and outer) are pushed into the ground. Air Delivery: Compressed air is pumped down through the space between the tubes. Cuttings Ejection: Compressed air rises, carrying rock cuttings (from drilling) through the inner tube of the drill rods. Sample Collection: Rock cuttings are separated from the air via a cyclone system, and samples are collected for analysis. RC Rig Components: Dual-tube drill rods: These are the tubes that descend into the drilling rig. Drill Hammer: This is the tool that breaks up the rock. Drill bit : Typically made from hardened steel or tungsten carbide, designed to crush and break rock. Air Pump: Provides the compressed air needed to propel the rock cuttings. Cyclone System: Separates the rock cuttings from the air. The importance of reverse circulation drilling (RC rig): Sample extraction: Fine samples of rock can be extracted for analysis. Mineral locating: Samples can be used to locate minerals such as gold and other metals. Deep penetration: can reach significant depths, enhancing exploration capabilities. Reverse circulation drilling: Air is sent through the space between the tubes and forces rock pieces through the inner tube. The most important informations we should collect while working on the rigs are : - quick description of ( lithology, alteration, associated minerals appearance ). - starts and ends of our expected mineralised zones approximately. - split and prepare samples and its weight . - Resource Assessment : Aids in quantifying mineral resources estimation. - faster drilling rates: general quicker than diamond drilling, making it cost-effective for exploration. Note : The drilling rate different by the rock unites and its hardness. In short, an RC rig is an effective tool in mineral exploration, as it allows accurate rock samples to be extracted and analyzed and faster and more economical and less cost than other drilling methods .

Data centers are facilities that house computer systems, including servers, to store and manage data. Demand for data centers is rising, especially to support new AI technologies that are computationally intense. Data centers require large amounts of energy and mineral resources to build, including critical minerals. The U.S. relies on imports from other countries for many of these minerals. Here are some of the key minerals essential to data centers. How to Read This: Here are key minerals used in each part of the data center, and the percent the U.S. imports to meet consumption for that mineral (based on the USGS Mineral Commodity Summaries 2025). Serverboards and Circuitry: Serverboards connect the essential electrical components of a server. These, and other intricate circuitry, require minerals that are able to efficiently conduct electricity and are resistant to corrosion - especially copper. Silver - 64% Gold - 0% Copper - 45% Tin - 73% Tantalum - 100% Palladium - 36% Platinum - 85% Heat Sinks: Heat sinks prevent servers from overheating by transferring heat away from sensitive electronics. These metals are highly conductive, malleable and resistant to corrosion. Aluminum - 47% Copper - 45% Semiconductors and Microchips: Microchips and processors are the “brains” of data centers. They work because of semiconductor materials like silicon and germanium that conduct electricity under specific conditions. Arsenic - 100% Flourspar - 100% Gallium - 100% Germanium - 100% Indium - 100% Palladium - 36% Platinum - 85% Silicon - 75% Boron - 0% Rare Earth Elements - 80% Data centers are energy-intensive. In addition to minerals required to build data centers, mineral resources are also used in energy production and storage to power data centers. Between 2014 and 2024, data center load growth tripled. By 2028, data centers could consume 12% of total U.S. electricity (Shehabi et al. 2024, United States Data Center Energy Usage Report, https://doi.org/10.71468/P1WC7Q). The USGS tracks over 90 mineral commodities in 180 countries to provide information about the supply risks of, demand for, and flow of minerals used in diverse sectors of the economy. Source: https://www.usgs.gov/media/images/key-minerals-data-centers-infographic

The JORC Code and NI 43-101 are two prominent reporting standards used in the mining industry for resource estimation. Here is a comparison between the two: 1. **JORC Code (Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves)**: - **Region**: Primarily used in Australasia (Australia and parts of Asia). - **Main Principles**: - It focuses on providing clear guidelines for the reporting of exploration results, mineral resources, and ore reserves. - JORC requires transparent reporting and compliance with defined standards. - **Compliance**: Mandatory for companies listed on Australian stock exchanges. - **Categories**: - Exploration Results, Mineral Resources, and Ore Reserves are classified based on their geological certainty and economic viability. - **Competent Persons**: Reports must be prepared by a "Competent Person" who is a member of a recognized professional organization. 2. **NI 43-101 (National Instrument 43-101)**: - **Region**: Mainly used in Canada. - **Main Principles**: - It aims to standardize reporting on mineral properties to provide investors with clear information. - NI 43-101 ensures fair disclosure, accuracy, and transparency of mineral project information. - **Compliance**: Required for all Canadian companies listed on Canadian stock exchanges. - **Categories**: - Like JORC, NI 43-101 distinguishes between Exploration Results, Mineral Resources, and Mineral Reserves based on confidence levels and economic considerations. - **Qualified Persons**: Reports must be prepared by a "Qualified Person" who is required to be a professional geoscientist or engineer. In summary, both the JORC Code and NI 43-101 are reporting standards that aim to ensure transparency, accuracy, and consistency in reporting exploration results, mineral resources, and ore reserves. While they have similarities in the classification of resources, the main difference lies in the terminology used and the specific requirements for reporting in each standard.

In mining, planned vs. actual reconciliation is much more than a monthly report: it is the “thermometer” of the operation. 👉 For the company, it means evaluating operational efficiency and adjusting costs. 👉 For the mine, it shows how closely planning adheres to real mining conditions. 👉 For investors, it is proof of transparency and predictability of results. In general, the process consists of comparing: What was planned (block models, mine sequencing, production targets); With what was executed (moved volumes, actual grades, delivered production). This analysis allows deviations to be identified, models to be corrected, sequencing to be improved, and resources to be optimized. In coal mining, reconciliation becomes even more critical: ⚒️ Quality variations (ash, moisture, calorific value) can directly impact contracts and financial outcomes. ⚒️ Quick adjustments ensure that planning remains aligned with plant or market requirements. In the end, reconciliation is not just a control measure but a continuous learning process that builds trust across the entire mining value chain. And this trust is only possible when the starting point — the block model — is properly validated. After all, there is no reliable reconciliation without a solid model, just as there is no efficient planning without reconciliation to test and feed it back.

In mining, success depends not just on what we extract but on how well we do that. And to optimize the mining process, we need to understand the orebody. A.k.a Orebody knowledge. Orebody knowledge is not just a buzzword. It encompasses a wide range of fields (geological, geotechnical, geochemical, and metallurgical) that described the characteristics of a deposit. It’s the backbone of informed decision making across the mine life cycle. Exploration to closure. ✅ Why it matters: - Accurate resource estimation - Safer and more efficient mine designs - Optimized extraction and scheduling - Reduced operational risk - Improved processing and recovery - Safer waste rock and tailings disposal Geotechnical characterization and domaining is just one part aspect of "Orebody Knowledge" but it is a key contributor to optimize the mine design/extraction sequence and execute it safely. The image (Sala & Santos 2022, modified from Read & Stacey, 2009) below is a great illustration of the typical inputs to a geotechnical domain model. Deposits and mining methods differ, and the geotechnical characterization is adjusted accordingly. As it should be. But here’s the question I’d love to hear your thoughts on: Which aspect of geotechnical characterization and orebody knowledge do you think is most overlooked? As an example: microdefects/veining is not explicitly shown in the figure while it has a material impact on porphyry deposits. Which aspects should we pay more attention too?

Large diameter raise boring is commonly used for developing cylindrical vertical to inclined shafts in underground mines. The circular cross section is favourable from a geotechnical perspective, and a significant benefit of raise boring is an improvement in safety with the removal of personnel to work directly in the shaft during development. However, unlike conventional shaft sinking, if ground conditions are poor, there is no generally little or no opportunity to install support until the shaft is completed. As such, shaft conditions need to be well understood prior to commencing development to determine whether the shaft can remain stable throughout the back-reaming process, or stable with pre-support. An alternative location needs to be sought if conditions in the selected location are not amenable to raise boring at the desired diameter, otherwise the shaft might collapse, and the expensive gear lost. Unsafe conditions could also arise if conditions are not amenable. The geotechnical investigation requirements will vary depending on the shaft development method and geotechnical conditions at the shaft location. For the highest confidence in results, the investigation drillhole should be located as close as possible to the planned shaft axis (within 10 m) and hole deviations should be understood when assessing the confidence of results along the length of the shaft. However, there are circumstances where 10 m can be too large, such as the presence of sub-vertical adverse geological structures which might be intersected by the raise but not by a drillhole placed along the raise axis. There would be a residual risk that an unknown subvertical structure close to the raise can lead to significant failure or falloff. If there was a history of this at the site, there might be a case to drill inclined holes to confirm that there are no adverse dykes or structures near the proposed raise, at least in the weathered zone. Although the investigation drillhole should be positioned as close as possible to the planned raise, it is important to consider that reaming cannot be conducted through drill rods if they become stuck in the hole. If that is considered to be a risk, the cover hole should be stepped off by about 5 m. Other considerations for data confidence include core loss, whether from navigational drilling (which should be avoided) or from poor ground conditions. HQ triple tube drilling should be considered throughout any weathered or weak rock zones and in all cases, careful core handling and marking of drillers’ breaks should be diligently applied. In some circumstances, the geotechnical investigation drillhole might not be required. For example, at a mine where there is already significant experience in raise drilling and the geological and geotechnical conditions are already well known (and consistent), investigation drillholes are not required for shorter and smaller diameter raises (such as escapeways). However, for any large diameter raises, the risks associated with low data confidence should be well understood if a shaft investigation drillhole is not undertaken. Comprehensive practical guidance on raise bore and shaft geotechnical analysis is presented in Mine Mentor’s module on the topic. To learn more, visit: https://mine-mentor.com/learning/

Resumo: Em depósitos polimetálicos, a presença de múltiplos elementos economicamente valiosos dificulta a análise comparativa e a classificação de recursos. O conceito de teor equivalente surge como uma ferramenta fundamental para integrar diferentes metais a partir de um único parâmetro de valor, facilitando a avaliação técnica e econômica. Este artigo discute os fundamentos do cálculo de teor equivalente, sua aplicação em diferentes contextos geoeconômicos, limitações práticas e exemplos reais de sua utilização na indústria mineral. 1. Introdução Depósitos polimetálicos, como os de Cu-Au-Ag, Zn-Pb-Ag ou Cu-Mo-Au, representam desafios singulares para engenheiros de minas e geólogos econômicos devido à diversidade de elementos com diferentes valores de mercado, custos de recuperação e comportamentos metalúrgicos. Para facilitar a avaliação econômica e a comparabilidade entre blocos ou domínios de um depósito, utiliza-se o conceito de teor equivalente (equivalent grade), convertendo todos os teores ao equivalente de um único metal de referência. 2. Conceito de Teor Equivalente O teor equivalente representa o teor de um único metal que geraria o mesmo valor econômico que a combinação de metais presentes. Ele permite, por exemplo, expressar teores de ouro e prata em termos de equivalente ouro (AuEq) ou cobre e molibdênio em termos de equivalente cobre (CuEq). A fórmula básica do teor equivalente é: 2.1 Tipos de Teor Equivalente A aplicação do conceito de teor equivalente pode assumir diferentes abordagens conforme o objetivo do estudo, o nível de conhecimento do depósito e a natureza dos produtos gerados. Os principais tipos de teor equivalente incluem: a) Teor Equivalente Baseado em Preço de Mercado (ou Valor Bruto) Este é o tipo mais comum e representa o valor dos diferentes metais com base nos seus preços brutos de mercado, sem considerar custos de recuperação metalúrgica, transporte ou comercialização. Embora seja útil para comparações iniciais e classificações exploratórias, tende a superestimar o valor real dos componentes do minério. b) Teor Equivalente Metalúrgico (ou Teor Equivalente com Recuperação) Neste caso, as recuperações metalúrgicas são incorporadas no cálculo, tornando o resultado mais representativo da realidade de uma futura operação. É amplamente utilizado em estudos de viabilidade, estimativas de recursos e projetos com definição de rota de processo conhecida. c) Teor Equivalente Econômico (ou Teor Eq com Receita Líquida) Este é o mais completo e acurado, considerando não apenas recuperação metalúrgica, mas também custos operacionais (tratamento, refino, transporte, royalties, penalidades). Converte todos os elementos em valor líquido recuperável em termos de um único metal, podendo ser utilizado para análise econômica de cenários e comparação entre projetos. 3. Aplicações Práticas 3.1 Avaliação Econômica Preliminar Durante estudos de scoping e pré-viabilidade, o teor equivalente fornece uma estimativa simplificada de valor de um corpo mineral, ajudando na seleção de alvos de perfuração ou na modelagem preliminar de recursos. 3.2 Seleção de Cut-off É comum definir o cut-off em termos de teor equivalente, como por exemplo: 4. Exemplo de Cálculo Considere um depósito com os seguintes parâmetros: Teor Cu: 0,5%, Recuperação Cu: 90%, Preço Cu: US$ 9.000/t Teor Au: 0,4 g/t, Recuperação Au: 80%, Preço Au: US$ 2.000/oz 1 oz = 31,1035 g Convertendo o teor de ouro para equivalente cobre: CuEq(Au) = 0.4*(0.80*2000*(1/31.1035))/(0.90*9000) = 0.254% CuEqTotal (Au) = 0.5% + 0.254% = 0.754% 5. Limitações do Método Volatilidade de preços: Alterações nos preços de mercado afetam diretamente os teores equivalentes. Recuperações distintas: Nem todos os metais têm recuperações previsíveis ou constantes. Penalidades metalúrgicas: Certos elementos podem impactar negativamente o processo ou o concentrado final. Riscos jurídicos/fiscais: A comercialização de determinados metais pode ser mais onerosa em certas jurisdições. 6. Considerações Finais O uso de teor equivalente é uma prática consolidada na engenharia de minas, sendo uma ferramenta essencial para análise de viabilidade técnica e econômica de depósitos complexos. No entanto, seu uso deve ser criterioso, sempre baseado em premissas atualizadas de preço, recuperação e fluxo de processo. Referências CRIRSCO (2019). International Reporting Template for the Public Reporting of Exploration Results, Mineral Resources and Mineral Reserves. SME (2011). Mining Engineering Handbook. AusIMM (2020). Monograph 30 - Mineral Resource and Ore Reserve Estimation. JORC Code (2012). Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves. David Drumond - Engenheiro de Minas/ Doutor em Engenharia de Minas, Metalurgia e Materiais

Definir correctamente tus Objetivos y KPIs en RRHH, es clave para impulsar los Objetivos de tu Negocio. Pir lo general deberás superar, al menos estos 11 Obstáculos y Errores Comunes para Definir y Medir Objetivos y KPIs en RRHH. 1. Falta de alineación estratégica: Dificultad para vincular los objetivos de RRHH con los objetivos del Negocio. Poco dominio de las palancas que generan ingresos y gastos en el Negocio 2. Falta de una metodología práctica y coherente: Tanto para definir Objetivos y KPIs, como para medirlos y hacer análisis posteriores. Te recomiendo mi Método JUANCA, que incluye 4 componentes: Preguntas Claves a Clientes Internos, Conversión de Quejas en Objetivos, Benchmarking y Menú Predeterminado con más de 600 Objetivos y KPIs en RRHH para que puedas escoger y adaptar a tu situación específica. 3. Datos insuficientes o poco fiables: Escasez de información precisa para medir el desempeño o establecer KPIs relevantes. 4. Resistencia al cambio: Empleados o líderes que se oponen a nuevos KPIs o procesos de evaluación. 5. Objetivos poco claros o ambiguos: Definir metas vagas que no son medibles ni alcanzables. 6. Falta de tecnología adecuada: Sistemas obsoletos que dificultan la recolección y análisis de datos. Te recomiendo mi MetroKPI, un software simple, directo, práctico, económico, que te ayuda desde la definición de tus Objetivos y KPIs en RRHH, hasta medirlos, evaluarlos, diseñar Programas Estratégicos de Mejoras, a medir el ROI Esperado y Real de los mismos, a hacer Reportes Analíticos que muevan a la acción…. 7. Prioridades cambiantes: Cambios frecuentes en la estrategia organizacional que desactualizan los KPIs. 8. Escasez de habilidades analíticas: Falta de personal capacitado en RRHH para diseñar e interpretar KPIs. 9. Cultura organizacional débil: No se fomenta la rendición de cuentas o el seguimiento de objetivos. 10. Enfoque excesivo en métricas de procesos: Ignoran KPIs financieros y de ROI. 11. Falta de comunicación interna: Desconexión entre departamentos que impide establecer KPIs colaborativos. Te puedo ayudar. ¿Qué obstáculo se te hace difícil superar? ¿Qué otros obstáculos estás enfrentando?

Since founding Objectivity I’ve discovered that resource drill planning is mostly being done with the assumption that more drilling will lead to more resource classification/definition. More drilling isn't always better in mining. Here is why. Like many things in mining this is not quite correct and the current process leaves a lot of value on the table. Budget is often set before a full technical assessment (as these tend to evolve during budgeting), there are no clear KPIs, boards/management tend to reduce budgets (because drilling is often the first casualty of cost control) and there are a limited number of options reviewed when making plans. Someone presents a plan with X number of holes, and often the only question asked is “can you add a hole here and there?” or “what can you do with half the budget?”. Resource development drilling and exploration drilling have very different value drivers. The former benefits from a QP/CP defined sampling criteria that will guide the resource drilling plan’s layout. Unfortunately spacing is a great criteria for a time when holes were planned in 2D on mylar. But what if I told you that in most cases, you could achieve the same expected resource conversion with 30% fewer drill holes, while meeting QP/CP sampling requirements? You'd probably say: "That's not possible." When we analyze resource conversion efficiency, we see a curve that looks like this: as you add more drill holes, the percentage of volume potentially converted increases, but not linearly. Drilling from 10,000 to 13,000 meters can improve resource conversion by 24%, but then adding another 3,000 meters yields only an additional 7% (data courtesy Adventus Zinc @Curipumba). The curve always flattens dramatically after a certain point, reducing incremental value. This is the investment curve that highlights a critical point of diminishing returns that most drilling programs blow right past. This is sensitivity analysis for your resource drilling - sensitivity analysis is always expected at the feasibility study stage yet seldom used for an activity that is on every mining/late stage exploration project’s critical path. Would you make better decisions with multiple plans objectively assessed against key criteria, or with just a single plan? The answer seems obvious, yet most companies stick to the single-plan approach citing, “no time”, “no people”, “shifting criteria”. Still skeptical? Good, skepticism is healthy. But why not put us to the test? We are confident that we can change how you think about resource drilling and the value that it produces. Increasing conversion efficiency, while respecting QP/CP requirements, will make our industry more financially efficient. Show us your data and we'll show you a range of solutions to help make objective based investment decisions. Begin optimizing your investment here: https://docs.google.com/forms/d/e/1FAIpQLSe8JdRWRB5MK9gnzJbk3FQZu17dFmWyUkre8S2yXo_k0l2HIw/viewform

I've been thinking about why innovation in mining moves at such a glacial pace compared to tech, and it comes down to a fundamental reality that many industry consultants miss: mining is a small market with unique constraints that require different innovation approaches. When Apple first reached a $1 trillion valuation, every major mining company was about 1/8 its size. Fast forward to 2021, and BHP (the largest mining company in the world) had become 1/19th the size of Apple. Today in 2025, Apple sits at around $3 trillion while the largest mining companies have not grown in comparison. When Apple allocates just 1% of its value to R&D, that's $30 billion, more than many mining companies' entire market capitalization. They can afford to have centralized innovation teams with thousands of specialists working on next-generation technologies. They can afford to fail repeatedly because the successes will more than pay for the failures. Mining companies don't have that luxury. We can't use the same processes as Apple or Google or any tech giant because we don't have the same resources or market dynamics. For us to imagine that we can compete with Apple on a level playing field for innovation is ridiculous. This reality creates several challenges that are unique to mining innovation: First, we can't afford the same failure rates. When a technology company launches ten innovations and two succeed wildly, that's considered a massive win. In mining, we need eight or nine out of ten to succeed just to justify the investment. Second, we can't afford specialized innovation teams at the same scale. While technology companies have thousands of people focused solely on innovation, mining companies might have a handful, if they have dedicated innovation staff at all. Third, our innovation cycles are necessarily longer. We can't just push a software update to millions of users overnight. Implementing new technologies in mining involves physical infrastructure, regulatory approvals, and operational changes that take time. Fourth, our market for any specific innovation is minuscule. A tech company can sell a successful product to billions of enthusiastic consumers or millions of businesses. A mass market innovation can also be funded completely by advertising allowing it to be “free”. A mining innovation might only have a few hundred potential customers distributed around the world. It doesn't mean we should give up on innovation, quite the opposite. Instead of imitating models from tech industries that operate under entirely different dynamics, mining needs frameworks tailored to our specific constraints. Rather than criticizing ourselves for not innovating like Apple or Google, we must embrace our industry’s realities. Mining innovation should serve clear value creation, not become an end in itself.

Kriging is a geostatistical technique used to predict unknown values at specific locations based on nearby observed data. It is widely used in fields like environmental science, mining, and machine learning for accurate spatial predictions. When applied correctly, kriging can significantly improve prediction accuracy. ✔️ Accurate Predictions: Kriging considers distance and direction of data points for precise estimates. ✔️ Error Minimization: Reduces prediction error by weighting observations based on spatial correlation. ✔️ Uncertainty Assessment: Provides confidence intervals to assess prediction reliability. ✔️ Flexible Methods: Supports ordinary, simple, and universal kriging for different data patterns. ❌ Computational Intensity: Can be demanding for large data sets due to matrix operations. ❌ Overfitting Risk: Complex models might capture noise instead of actual patterns. ❌ Assumption Sensitivity: Relies on assumptions about stationarity and data distribution. ❌ Edge Effects: Predictions near data set edges can be less reliable. The visualization below illustrates simple kriging as the mean and envelope of Brownian random walks passing through the data points. Visualization adapted from Wikipedia: https://en.wikipedia.org/wiki/Kriging# 🔹 In R: The gstat and automap packages support variogram modeling and kriging, while geoR allows Bayesian kriging. 🔹 In Python: Use pykrige for kriging and scikit-learn for Gaussian process regression. geopandas handles spatial data, and dask manages large-scale computations efficiently. https://www.linkedin.com/in/joachim-schork/

Geotechnical engineering plays a vital role in mine design and safety. It’s not just a support function — it ensures stability across both open pit and underground operations. As part of a geotechnical study conducted at the Sukari Gold Mine in Egypt — and with guidance from experienced geotechnical engineers on-site — I worked on assessing the stability of crown pillars, which serve as the rock mass separating surface and underground mining levels. The assessment involved: Rock mass classification using the Q-system Analysis of joint conditions, RQD, groundwater, and stress environment Evaluation of safety using stability charts and Factor of Safety calculations Poorly designed crown pillars can lead to hazards such as instability, overbreak, or ore dilution due to unplanned rock mass movement — directly impacting both safety and production efficiency. This experience emphasized the importance of turning geological data into engineering decisions that protect people, equipment, and long-term operations.

To ensure continuous excavator operation with no idle time, the required number of dump trucks must be calculated using: Rule of thumb Number of Trucks = Truck Cycle Time ÷ Excavator Loading Time Example: Truck cycle time = 8 minutes Excavator loading time = 2 minutes → 8 ÷ 2 = 4 trucks (theoretical) However, real-world operations involve delays: -Dumping queue -Road conditions -Minor inefficiencies That's why we always add a buffer truck to avoid excavator downtime. So in this case: 5 trucks would be required in practice. Proper fleet balance ensures higher productivity and reduces fuel waste and idle time.

Ever wondered how smart investors pick winning stocks? They use stock market ratios – simple calculations that reveal a company's health. Let's break down the most useful ones. 𝗘𝗮𝗿𝗻𝗶𝗻𝗴𝘀 𝗥𝗮𝘁𝗶𝗼𝘀 The Price-to-Earnings (P/E) ratio shows how much you're paying for each dollar of profit. A high P/E might mean investors expect growth, but it could also signal an overvalued stock. The Earnings Per Share (EPS) tells you the profit allocated to each share. Rising EPS usually means the company is growing. Why they matter: These ratios help you avoid overpaying for stocks. Think of them as the price tag checker of investing. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗥𝗮𝘁𝗶𝗼𝘀 The Debt-to-Equity ratio reveals how much a company relies on borrowing versus owner funding. Lower is generally safer. The Interest Coverage ratio shows how easily a company can pay its debt interest from its earnings. Why they matter: They're like checking someone's credit score before lending them money. High debt can sink even profitable companies during tough times. 𝗥𝗲𝘁𝘂𝗿𝗻 𝗥𝗮𝘁𝗶𝗼𝘀 Return on Equity (ROE) measures profit generated from shareholder investment. A higher ROE means better use of investor money. Return on Assets (ROA) shows how well a company uses its assets to make profit. Why they matter: These ratios reveal management efficiency. Good managers create more value from the same resources. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗮𝘁𝗶𝗼𝘀 The Price-to-Cash Flow (P/CF) ratio compares a stock's market price to its cash flow. A lower ratio might signal an undervalued company. PEG ratio shows your value based on the P/E ratio and earnings growth rate. Why they matter: They help you compare value across different companies and find income-generating investments. 𝗣𝘂𝘁𝘁𝗶𝗻𝗴 𝗜𝘁 𝗔𝗹𝗹 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 No single ratio tells the whole story. Smart investors use combinations of ratios. For example, a stock with low P/E, low debt, high ROE, and good dividend yield could be a hidden gem. These ratios aren't crystal balls, but they are powerful tools. They turn complex financial statements into simple numbers you can use to make better decisions. Start using them in your research, and you'll see the market in a whole new way.

El Futuro de la Minería a Cielo Abierto: Inteligencia Artificial y Equipos Autónomos como Claves de la Eficiencia La industria minera está atravesando una transformación impulsada por la inteligencia artificial (IA) y la automatización, tecnologías que están redefiniendo la forma en que operan las minas a cielo abierto. Estas innovaciones no solo buscan incrementar la productividad, sino también minimizar errores humanos, reducir costos operativos y mejorar las condiciones de seguridad. 1. Optimización de la Producción con Inteligencia Artificial Los algoritmos de IA son capaces de analizar grandes volúmenes de datos geológicos, topográficos, operacionales y climáticos en tiempo real. Esta capacidad de procesamiento permite: • Optimizar los planes de minado, ajustando los diseños según la ley del mineral, los costos de extracción y las condiciones del terreno. • Predecir el comportamiento de los equipos, reduciendo paradas no programadas gracias al mantenimiento predictivo. • Tomar decisiones automatizadas, priorizando zonas de mayor rentabilidad sin intervención humana directa. Esto reduce los tiempos de respuesta ante cualquier cambio en el entorno operativo, haciendo más dinámico y eficiente el ciclo minero. 2. Camiones de acarreo autónomos Los camiones sin conductor operan con precisión milimétrica, siguiendo rutas programadas que evitan accidentes, reducen el desgaste de las vías y maximizan la eficiencia del transporte. Entre los beneficios destacan: • Disminución del error humano, especialmente en tareas repetitivas o de largas jornadas. • Operación continua 24/7 sin fatiga. • Consumo de combustible más eficiente debido a patrones de conducción optimizados. 3. Perforadoras autónomas Las perforadoras automatizadas aseguran precisión en la ubicación, profundidad y orientación de cada barreno. Esto tiene efectos directos en: • Mejor fragmentación del material, lo que reduce los costos de tronadura y acarreo. • Mayor seguridad, ya que se evita la exposición del personal a zonas de alto riesgo. • Estandarización del proceso, reduciendo las variaciones de calidad. 4. Drones para topografía y monitoreo Los drones equipados con cámaras de alta resolución y sensores LIDAR o multiespectrales permiten realizar: • Mapeos rápidos y actualizados de la mina, mejorando el control volumétrico y el seguimiento del avance. • Inspecciones en zonas peligrosas o de difícil acceso sin exponer al personal. • Monitoreo ambiental y de estabilidad de taludes, previniendo deslizamientos o eventos geotécnicos. 5. Beneficios generales para cualquier mina a cielo abierto • Reducción de costos operativos y de capital al disminuir la necesidad de intervención humana y errores asociados. • Mayor seguridad en todas las operaciones, especialmente en entornos remotos o extremos. • Mejor control de los indicadores de producción, al tener datos en tiempo real. • Menor huella ambiental, gracias a decisiones más precisas en la planificación y ejecución.

OSHA provides a comprehensive framework of exposure limits, design standards, and operational guidelines to minimize risk in workplaces across industries. From permissible noise levels (90 dB(A) over 8 hrs), to chemical exposure limits (e.g., Silica – 50 µg/m³ TWA), and safe oxygen ranges (19.5%–23.5%), these limits are non-negotiable foundations of workplace safety. 📊 Adhering to OSHA's safety parameters ensures: ✔️ Reduced occupational health risks ✔️ Legal compliance and audit readiness ✔️ Safer, more productive work environments

Artificial intelligence (AI), the capability of computational systems to perform tasks typically associated with human intelligence, is without question one of the most far-reaching and impactful technological developments of the 21st century. From chatbots, online shopping recommendations and financial risk assessment to analysis of customer data, artificial intelligence is a game-changer for industries large and small. One industry rarely associated with artificial intelligence is mining; yet the nexus between AI and this industry—especially for Latin America—deserves increased attention, given its profound impact in both developed and emerging markets. For example, AI is capable of analyzing large amounts of data and providing digital solutions for businesses, with the technology helping to increase speed and safety in mining operations Mining is the backbone of the natural resources sector in Latin America with Chile, Peru, Brazil and Mexico the dominant players. The mining sector produces revenue exceeding $110 billion, accounting for 21% of the global base metal mining market, with copper, iron ore, and gold leading the way. The positive impacts of AI on the mining sector are threefold. First there is increased efficiency and cost reduction. AI-driven systems can predict when mining equipment will fail, reducing downtime and maintenance costs. For example, Rio Tinto, the British-Australian multinational, uses AI to monitor its fleet of autonomous haul trucks, reducing maintenance costs by up to 15%. Second is the factor of improved safety. AI can analyze environmental conditions to detect risks such as gas leaks, rockfalls, and structural failures. Take Newmont Goldcorp. The firm uses AI in underground mines to monitor for hazardous conditions. Finally, there is the impact of enhanced sustainability and compliance. AI improves ore detection and sorting, reducing waste and environmental impact. A good example is TOMRA’s AI-based ore sorting technology reduces water and energy usage in mines by 30%. Recognizably, AI also produces negative impacts in mining. High costs and barriers to adoption immediately come to mind. AI systems require significant upfront investment in hardware, software, and training. For example, the estimated cost of AI-powered autonomous truck fleets is $5 million–$10 million per vehicle. Another downside is workforce displacement. AI-driven automation reduces the need for human labor, particularly in routine mining jobs. In the case of Fortescue Metals Group’s transition to autonomous trucks, the company eliminated over 1,000 jobs in Australia. Many of the remaining workers needed to be reskilled. In general, workers must be retrained to manage AI-based systems, but many companies lack training programs. Also, of increasing concern surrounding AI are data privacy and cybersecurity risks. AI systems in mining operations are potential targets for cyberattacks, which could disrupt production. Three years ago a cyberattack on a mining company in Canada temporarily shut down operations. Not to be overlooked are ethical concerns enveloping AI. AI-driven decision-making in resource allocation and environmental compliance can raise ethical issues if not properly regulated. At a country level, Colombia provides an excellent case of AI applications. For example, wearable devices integrated with AI can monitor the health and safety of miners; and AI can be employed for the early prediction of potential methane explosions in underground coal mines. The 2021 explosion of the Tópaga mine, where 12 miners lost their lives, was reconstructed using AI with evidence revealing that the use of individual methane detectors could have displayed data alerting miners to potential risks. An increasing number of mining companies in Colombia are embracing AI. Drummond, one of Colombia’s largest coal producers, is using AI to enhance operational efficiency and safety. The company has been incorporating AI in predictive maintenance for mining equipment and optimizing the coal extraction process. Cerrejón, a major coal mining company in Colombia, has been experimenting with AI technologies to improve resource extraction efficiency and sustainability. And EPM (Empresas Públicas de Medellín), a Colombian energy and mining company, has implemented AI solutions in their mining operations, particularly in their hydroelectric and mineral extraction projects. The widest use of AI is for predictive maintenance, equipment monitoring, and operational optimization. What does the future hold for AI in the mining industry—for Latin America and beyond? As infrastructure improves, more mines in Africa, Latin America, and Asia will adopt AI to enhance efficiency and safety. Companies will use AI to discover new mineral deposits, reducing exploration costs by up to 50%. For example, GoldSpot Discoveries uses AI to identify gold deposits with high accuracy. We can also expect government to push AI-driven sustainability solutions to reduce mining’s environmental footprint; and more sophisticated AI-driven robots will handle complex mining tasks in hazardous environments. In essence, AI will continue to revolutionize the mining industry, enhancing efficiency, safety, and sustainability by automating tasks, optimizing operations, and improving decision-making through data analysis and predictive analytics. By 2035, AI is expected to generate over $100 billion in efficiency gains for the mining sector, with developed markets leading in automation and emerging markets catching up through AI-driven exploration and ore processing innovations. In resource-rich regions like Latin America, mining firms like Glencore, Zijin Mining Group, BHP, and AngloGold Ashanti are well-positioned to harness AI to the benefit of their shareholders and the public at large. ___________________________________________________________________________________________________ Jerry Haar is a professor of international business at Florida International University. He is also a fellow of the Woodrow Wilson International Center for Scholars in Washington, D.C., and the Council on Competitiveness. Eva Cristina Manotas R. is dean of the School of Mines and Full Professor at Universidad Nacional de Colombia. (This article first appeared in Latin Trade on March 31, 2025).

In the twenty-first century, there has been a growing awareness of the fundamental role global mineral resources play in driving technological advancement, especially within the context of the clean energy transition. This transition involves gradually replacing traditional fossil fuel–based sources such as oil, natural gas, and coal with renewable and low- and zero-CO2 technologies such as electric vehicles, storage batteries, wind turbines, and solar panels. Mineral resources are essential for sustaining the production of materials needed for these technologies and energy storage systems. Thus, the conversion from fossil fuels to low-carbon energy sources will depend on minerals. From a historical perspective, energy transitions require significant mineral resources and can vary greatly in speed, ranging from century-long transformations to shifts occurring within a few years. As an example, three centuries ago the number of elements used in energy technology such as windmills was limited to less than five (Figure 1). The array of elements used in energy applications has grown significantly since the 1800s, first due to the invention of the steam engine during the industrial revolution and then by the development of the combustion engine and in the mid-twentieth century the initiation of the nuclear era. But it is since the 2000s that the number of elements used in technology has increased exponentially, with low-carbon energy technology materials requiring a great diversity of chemical elements (Figure 1). Today, the element diversity is prevalent across most products and applications. For instance, of the 83 stable elements in the periodic table, at least 70 of them can be found in smartphones, and a similar number can be found in electric vehicles. Critical minerals—essential for modern economies and emerging technologies and often subject to supply constraints—emerge as a crucial component for sustainable development and supply chain stability. These resources, mainly metals, have become the backbone of various twenty-first-century industries including electronics, telecommunications, renewable energy, and transportation, making their consistent supply key for societal advancement. At the forefront of reducing carbon emissions and mitigating the effects of climate change are electric and hybrid vehicles, photovoltaic cells, wind turbines, batteries, and efficient lighting. These technologies are driving a growing global demand for energy-related critical minerals. This review centers on critical minerals. Due to the broadness of the topic, our focus is set on the energy-related metals copper, cobalt, nickel, lithium, and rare earth elements (REE). We focus on these elements because they are essential for renewable energy technologies and will drive exploration efforts during this century. We start by defining critical elements and minerals and their importance to the energy transition. We then describe their global occurrence, distribution, and ore deposit types, highlighting the role of economic geology and the minerals industry in sustaining a supply chain for the energy transition. This contribution complements other available reviews that further elaborate on various environmental, metallurgical, economic, and geopolitical aspects of critical minerals (see the sidebar titled Mineral Resources Sustain Infrastructure, Economy, and Life) Source: https://www.annualreviews.org/content/journals/10.1146/annurev-earth-040523-023316

Lectura esencial para quienes miden, proyectan y construyen el territorio Me permito recomendar una obra que ha marcado generaciones de ingenieros topógrafos, constructores y proyectistas: el “Tratado de Topografía” de Raymond E. Davis, acompañado por Francis S. Foote y Joe W. Kelly. Más que un simple libro, este compendio es un pilar académico y técnico que atraviesa con rigor los fundamentos, métodos modernos e históricos, instrumentos y aplicaciones de la topografía clásica y aplicada. 📚 En su quinta edición, cuidadosamente traducida por el Dr. José María Mantero, este tratado no solo sirve como texto para estudiantes, sino como una herramienta de consulta permanente para profesionales. Su enfoque exhaustivo abarca desde el uso del teodolito y la cinta hasta los levantamientos fotogramétricos, caminos, minas, obras civiles y medición de caudales. Con ejemplos, problemas de campo, fundamentos astronómicos y prácticas de gabinete, logra un equilibrio entre teoría y experiencia. 🌍 Como docente y profesional, valoro especialmente su énfasis en la precisión, trazabilidad y responsabilidad del topógrafo frente a la sociedad. Es un recordatorio de que cada plano, cada curva de nivel y cada punto georreferenciado tienen impacto en las decisiones territoriales, ambientales y técnicas. 📐 Si trabajas en diseño de infraestructura, catastros, obras civiles, hidráulicas o minería, este tratado sigue siendo una fuente confiable y profunda para revisar conceptos, resolver problemas y formar nuevas generaciones con base sólida. 🔗 La topografía no ha pasado de moda: se ha modernizado, se ha digitalizado, pero sigue siendo el arte y la ciencia de conocer la tierra con exactitud. Authors: R A Y M O N D E. D A V IS F R A N C I S S . F O O T E y J O E W . K E L L Y Profesores de Ingeniería en la Universidad de California Versión española de JO SE MARIA MANTERO Dr. Ingeniero Geógrafo

Before we move on to 2D estimation, one important step is to understand the behavior of thickness, grade, and accumulation. Do they exhibit spatial correlation? One way to assess this is by comparing them using a swath plot. Below is a swath plot we generated using Spyder (Python), which I’ll be sharing in my class this August. Looking at this figure — what are your thoughts? Do you see a correlation between the variables? Do you think applying 2D estimation is necessary?

Opening a new mine or expanding an existing operation can be a challenging and daunting task. Aside from assessing and evaluating social-environmental concerns and designing the mining and material movement approach, the first question often asked is, "how much will it cost us to mine?" This may need to be determined even before you decide that there is a potential project. Mine cost estimation may be done at many levels. At first it may be a simple “back of the envelope” estimation using similar operations to benchmark against. Later it may be decided to use an existing mine that the company owns and factor and compare costs against them. In the final stages a detailed bottom-up estimation based on first principles may be completed. This paper will investigate common methodologies of estimating operating costs for mines and present examples from actual operations and why those methods were selected. It will highlight why some methods are superior to others. Finally, we will explore the potential pit falls in cost estimation that often occur and the opportunities that may exist to lower mine costs.

This comprehensive guide explores the complexities and innovations involved in designing and planning underground mining projects. With surface resources declining and environmental standards tightening, underground mining is becoming the preferred method—expected to surpass 55% of global production by 2025. Key themes include: Fundamentals of Underground Mining: Emphasizes tailored designs for each deposit, with a focus on maximizing mineral recovery and minimizing waste and environmental impact. Modern Design Principles: Moves away from rigid, deterministic models to flexible, scenario-based planning using advanced 3D tools and sensitivity analysis, improving efficiency and profitability. Challenges and Opportunities: Covers critical issues like geomechanical stability, access design, ventilation, dilution, and recovery. It highlights the growing role of backfilling and real-time data in managing risks and improving sustainability. Software Integration: Critiques the fragmented nature of current tools and calls for fully integrated systems to support smarter decision-making and streamline operations. Method Selection and Optimization: Offers frameworks (like the Nicholas and UBC methods) for choosing mining methods and optimizing stope design, production schedules, and infrastructure. Access and Infrastructure: Details criteria for shafts, ramps, and galleries, and their role in supporting safe, efficient, and flexible mine development. Sustainability and Closure: Emphasizes the need for early planning of mine closure, using proven technologies and designs that support long-term environmental and economic sustainability. Overall, the document promotes a forward-thinking approach to underground mine design, combining technical rigor with strategic adaptability.

Problem Statement: A batch of parts was rejected due to an oversized hole diameter. 5-Why Analysis: 1.Why was the batch rejected?→ Because the hole diameter was larger than the specified tolerance. 2.Why was the hole diameter too large?→ Because the drilling machine was not properly adjusted. 3.Why was the machine not properly adjusted?→ Because the operator used an outdated setup sheet. 4.Why did the operator use an outdated setup sheet?→ Because the latest revision was not available at the machine. 5.Why was the latest revision not available at the machine?→ Because there is no system in place to ensure controlled document distribution. Root Cause: No document control system for distributing updated setup sheets. Corrective Actions: •Introduce a document control procedure to issue and display the latest revision only. •Restrict access to outdated setup sheets by removing old versions from machines. •Train machine operators and line leaders on verifying document revision before setup. Preventive Measures: •Digitize all setup sheets with access through a centralized network folder or MES (Manufacturing Execution System). •Implement revision control logs with sign-off for updates and acknowledgments by operators. •Conduct regular audits on setup documents at workstations. •Establish standard work that includes a revision check step before every job setup. •Integrate barcode or QR code scanning to verify correct document versions at machines.

The Real Risk for Resource-Rich Nations Isn’t a Lack of Minerals It’s a Lack of Vision In mining, the greatest threat isn’t depletion. It’s moral erosion. Across many resource-rich nations, the same pattern repeats: A few powerful actors prioritize personal gain, signing unfavorable deals that benefit their circle while the broader population is left behind. Let’s be clear: international companies aren’t the villains. They pursue profit that’s business. The failure lies in governments that undervalue and mismanage their own wealth. Mineral abundance is only an advantage with the right governance. Otherwise, it turns into a liability. Natural wealth isn’t just underground. It lives in contracts, institutions, and decision-maker integrity. Small Deposits Are Not Small Opportunities Global investors chase mega-projects while overlooking smaller, more agile deposits full of untapped value. Many smaller asset holders can’t reach real investors. They end up with intermediaries who take control, while the original owners lose value and voice. This isn’t just a Mongolian issue it’s common in many developing nations, where policy ignores mid-tier assets despite their potential. A Forecast: The Era of Small Deposits Is Coming Large deposits bring regulatory delays and political risks. Smaller ones offer faster timelines, lower complexity, and local upside if linked to the right investment frameworks. By 2040, mineral development will likely shift toward distributed value: many small, connected projects, not just one big mine. Mongolia Is Well Positioned Mongolia has more than large reserves. Its geology is rich in high-grade, small to mid-sized deposits. What’s missing is a platform that connects them to serious investment. => Conclusion Small deposits are not small opportunities. Only small thinking makes them so. The value of a resource depends not on its volume but on how, by whom, and under what terms it’s managed. => Call to Action To governments: Support mid-sized deposits not just the giants. To investors: Don’t ignore smaller assets. To owners: Know your value. Don’t trade it for short-term deals. Mining victory comes not from what’s underground but from how wisely it’s brought to the surface. https://www.linkedin.com/in/temuujin-gankhuyag-9a0369360/

Don’t just track savings if you want top procurement I’d do this instead... Track metrics that align with your business goals. Group the KPIs for better decision-making: ➝ Quality KPIs – To assess supplier performance. ➡️ Compliance rate, Supplier defect rate, Purchase order accuracy ➝ Inventory KPIs – To assess effective order management ➡️ Inventory turnover ratio, Inventory carrying cost, Inventory aging ➝ Cost-Saving KPIs – Evaluate procurement's level of impact ➡️ Cost avoidance, Procurement ROI, Spend under management, Cost reduction ➝ Delivery KPIs – To assess a reliable supply chain ➡️ Vendor availability, Purchase order cycle time, Supplier lead times Most Procurement teams only track two or three of these metrics and then struggle to improve YOY or even to justify their value

Hoy quiero hablar de un concepto fundamental que impacta directamente en la planificación, costes y ejecución de nuestros proyectos: los Volúmenes Aparentes. Sabemos que 1 m³ de material in situ (en su estado natural) rara vez ocupa el mismo volumen una vez excavado, transportado o compactado. ¡Comprender estas variaciones es crucial! Tomando como base 1.0 el volumen del material in situ: 1️⃣ EXCAVACIÓN / VOLADURA: Tierras: Al excavar, el suelo se suelta y sufre un esponjamiento. Su volumen aumenta, pasando de 1.0 a aproximadamente 1.20 - 1.30. Roca (Canteras): Tras la voladura, la fragmentación es mayor. El volumen puede incrementarse significativamente, ¡llegando a 2.0 respecto al banco original! 2️⃣ CARGA Y TRANSPORTE: El material mantiene o incluso ajusta ligeramente su volumen esponjado. Tierras cargadas/transportadas: Se mantienen en el rango de 1.20 - 1.30. Roca volada cargada/transportada: El volumen puede ser de 1.25 - 1.50 (ya algo asentada respecto al momento justo post-voladura). 3️⃣ PROCESOS FINALES: RELLENOS Y COMPACTACIÓN (Tierras): Vertida (Rellenos sin compactar): Al descargar, el material se asienta un poco, situándose en 1.10 - 1.20. Pisada/Pre-compactada: Un primer paso de compactación lo lleva a 1.0 - 1.10. Compactada: Con la maquinaria adecuada, buscamos reducir vacíos y aumentar la densidad. El volumen final puede ser 0.95 - 1.0, ¡incluso ligeramente inferior al original si la compactación es muy eficiente! CHANCADO/TRITURACIÓN (Rocas): Chancado Primario: La roca procesada aún presenta un volumen superior al original en banco, alrededor de 1.30 - 1.40. Trituración (Fina): El producto final triturado sigue teniendo un volumen aparente mayor al in-situ, típicamente 1.20 - 1.30. ¿Por qué es vital este conocimiento? 📊 Cálculo de volúmenes: Para saber cuántos viajes de camión se necesitan. 💰 Estimación de costes: Afecta directamente los costes de excavación, transporte y disposición. 🛠️ Selección de maquinaria: El volumen real a manejar influye en la capacidad de los equipos. 📈 Planificación de obra: Optimiza los espacios de acopio y la secuencia de trabajos. Conocer y aplicar correctamente los factores de esponjamiento y compactación es un pilar para el éxito y la rentabilidad de nuestros proyectos.

Doing a detailed review of underground stope drill and blast performance can easily identify the main factors behind stope performances against designs. It is critical to review both technical and operational drill & blast aspects, identify issues and provide recommendations however it's more better to implement the recommendations to see improvements in the stope recovery. Below is a detailed stope drill and blast review I did and the summary and recommendations provided. Simple things that add up to an overall stope performance. ________________________________________________________________________ SUMMARY: • From the reviews done so far there is minimal to no issues identified in the technical design perspective in terms of drill and blast design parameters. • Void ratio was tight with 0.955 against site specific 0.95 after applying 30% swell factor. • From the LPU data, 5 slot holes were significantly undercharged. Prep returns & Simba cleanout plods show that these holes were prepped. There is possibility of slot bridge or significant slot underbreak expected as we do not have a breakthrough void yet at this stage. • Logger data and charge returns show that the correct number of dets were charged with correct delays assigned and correct number of primers charged as per the plan. There is no return to show that the primers were staggered as per the charge plan. • Total of 15.7t emulsion used. 14.8t emulsion charged into blast holes against a design of 14.2t. RECOMMENDATIONS: 1. D&B Engineers to check new void ratio using the CMS after undercuts taken to see if the downhole shot will be accommodated by the usable voids available. 2. D&B Engineers to notify slot location and the slot holes to the charge crews and stick to the approved charge plan. 3. Charge crews must be aware of how critical the slot is and notify engineers of any issues confronted during charging and assigning delays to the slot holes. This also includes other blast holes. 4. D&B Engineers to check LPU Data to make sure charging was done according to the charge plan issued. 5. LPU Operator to use LPU Hole ID same as Design Hole ID during charging to make it easier to compare ascharged LPU data against design data. 6. Shots greater than 20m height to be left for at least 3 hours (3hrs to confirm?) after charging to allow gasing of emulsion to the designed uncharged collars. 7. D&B Engineers to maintain reviewing of prep returns prior to finalizing charge plans.

Love this Periodic Table of Mining Careers - shows the diverse opportunities available to those that pursue a career in mining. Once again, we are proud to have our Next Generation Student program at Resourcing Tomorrow. If you are a student and would like to attend on a complimentary basis, or are a lecturer on course that has a relationship to mining (earth sciences, engineering, physical sciences, computer sciences, health & safety, finance, environment, social impact etc etc) please drop me a message or see the link below for more details https://resourcingtomorrow.com/nextgen

More and more people are discussing what constitutes a deep mine. Logically, many focus on depth below ground surface. But depth is just one aspect to consider and, as a surprise to many, not the dominant factor. Two of the more important aspects to consider are: (1) in situ stress state which varies globally ranging from conditions where the major principal stress is vertical to where the major principal stress is horizontal and multiples in magnitude of the vertical stress. For example, stresses in and around Pennsylvania can have horizontal to vertical stress ratios >4. a. Example 4000m depth where the major principal stress is vertical (e.g., deepest UG mine in South Africa is ~4000m) would have a Sigma 1 ~108MPa. b. Example 2000m depth where the major principal stress is horizontal and 2 times the vertical would also have a Sigma 1 ~108MPa. Both cases assuming 0.027MPa/m for vertical stress gradient. (2) If you couple the in situ stress state (for which depth is a variable) with intact rock strength, you have the makings of a parameter (the Stress Level Index, e.g., Kaiser et al 2000) that is valuable for defining deep mining conditions. The stress Level Index (SLI) relates the induced maximum stress (σmax) around an equivalent circular excavation in elastic rock to the rock’s intact compressive strength (σc), where σmax = 3σ1 – σ3 (with σ1 and σ3 representing the major and minor principal stress in the vicinity of the tunnel in the plane perpendicular to the tunnel axis). By benchmarking the SLI to real conditions (from these and more…Wilson 1971, Barton et al. 1974, Wiseman 1978; 1979, Hoek and Brown 1980a, Stacey and Page 1984, Jager et al. 1990, Kaiser et al. 1996, Board and Brummer 1997, Brummer 1998, and Martin et al. 1999, amongst others) you find: - Spalling initiates at σmax/σc= 0.3-0.5 (which corresponds with the unconfined rock mass strength near the excavation wall) - Notch formation with deep spalling (>20% of tunnel radius) and potentially minor strainbursting (spitting and popping) is to be expected for σmax/σc>0.5 to 0.6 - Moderate strainbursting and-or deep spalling at σmax/σc>0.6 to 0.8 - Major strainbursting and-or deep spalling at σmax/σc>0.8 This allows for a meaningful relative assessment of stressed ground challenges. In Figure 1, I have plotted my operating mine and project experience for cases available in the public domain to show the value of looking at SLI vs depth. One can quickly see that depth is not the driving factor and that it is a combination of in situ stress and intact rock strength. These plots are for the in situ stress before mining but induced stress at different times of mining can be used to assess SLI as well and see how it changes over a mine’s life. Furthermore, in the past, Sigma 1 / UCS was used as a stressed ground indicator. I argue that SLI is more valuable. When the same data is plotted with S1/UCS (Figure 2), there is limited range in the results reducing decision making potential. One of the reasons for this is the importance of the other principal stresses in assessing when deep mining challenges could be expected.

Blasting isn’t just a science—it’s an art that tests you in the field. Even with perfect calculations, things go wrong. But a skilled engineer? He plans for those moments. Here are 5 of the most common blasting issues—and how to solve them smartly and safely: ⸻ 🔹 1️⃣ Misfires – The Silent Threat A hole fails to detonate. It’s not just a delay—it’s a danger. 🚩 Causes: Faulty connections, wet explosives, poor initiation ✅ Fix: • Always double-check connections • Use reliable detonators • Respect re-entry wait times • Conduct post-blast inspections, every time ⸻ 🔹 2️⃣ Flyrock – The Uninvited Guest Rock fragments flying beyond control. One of the most hazardous outcomes. 🚩 Causes: Shallow holes, weak stemming, poor burden ✅ Fix: • Use proper stemming materials • Increase burden & spacing • Always apply blast mats near infrastructure ⸻ 🔹 3️⃣ Poor Fragmentation – Big Boulders or Fine Dust Bad fragmentation = high costs, unsafe loading, more secondary blasting. 🚩 Causes: Wrong charge, bad pattern design, uneven explosive loading ✅ Fix: • Optimize burden and spacing • Match explosive energy to rock strength • Ensure uniform loading and good stemming ⸻ 🔹 4️⃣ Excessive Ground Vibration – The Hidden Risk It can shake buildings, crack structures, and ruin your reputation. 🚩 Causes: High MIC, tight delays, sensitive ground ✅ Fix: • Reduce Maximum Instantaneous Charge • Increase delay timing • Move the blast farther from structures ⸻ 🔹 5️⃣ Overbreak / Backbreak – Losing Control at the Edges Fracturing beyond your final walls or benches? That’s wasted effort and safety compromised. 🚩 Causes: High edge charge, poor delays, no buffer holes ✅ Fix: • Use pre-split or buffer rows • Reduce charge near final walls • Apply proper timing between holes ⸻ 📌 Pro Engineer Mindset: Real improvement starts with analyzing every blast, learning from the field—not just the theory. 🔍 Drone imaging, blast software, vibration monitoring—it’s not luxury anymore, it’s smart engineering. ⸻ 💡 Field Pro Tip: If you’re near villages, roads, or weak geology—triple-check your plan. A small mistake here can cost your whole project. ⸻ 🔜 Next Up in the Series: ✅ Lesson 8: Real Blast Pattern Breakdown – A Full Case Study ✅ Lesson 9: Drilling & Loading – Best Practices from the Field

**Introduction** Blasting is an essential operation in surface mining that impacts productivity, safety, and environmental factors. To improve the quality of blasting, it is important to focus on optimizing design, minimizing adverse effects, and ensuring safety. This report outlines key strategies for enhancing blasting efficiency and effectiveness in surface mines. **Key Areas for Improvement** 1. **Blast Design Optimization** - **Hole Layout and Spacing**: Properly spacing and designing blast holes ensures optimal fragmentation and avoids inefficient or dangerous outcomes. This can be achieved by tailoring the blast design to the rock type and the mining objectives. - **Explosive Type and Quantity**: Using the right type of explosives and correct charge amounts helps to achieve the desired rock breakage while controlling costs and minimizing environmental impact. - **Timing and Detonation**: Employing advanced delay and electronic detonators can control the blast energy distribution, improving fragmentation and reducing unwanted side effects like excessive vibration. 2. **Fragmentation Control** - **Achieving Optimal Fragment Size**: Ensuring that the fragmentation is neither too fine nor too coarse enhances downstream processing, like crushing, and reduces operational costs. - **Real-time Monitoring**: Using tools like drones or laser scanners to measure blast outcomes helps fine-tune future designs and achieve better fragmentation consistency. 3. **Vibration and Environmental Control** - **Vibration Monitoring**: Installing seismographs to monitor ground vibrations ensures that they remain within safe limits, preventing damage to infrastructure and minimizing disturbances. - **Dust and Flyrock Mitigation**: Using dust suppressants, blast mats, or other containment measures helps to minimize environmental and safety risks associated with flyrock and airborne particles. 4. **Safety and Training** - **Employee Training**: Continuous training on safety protocols, proper blast design, and the use of new technologies ensures a safer and more efficient operation. - **Clear Communication and PPE**: Ensuring workers are equipped with the proper protective equipment and have clear communication regarding blast schedules is crucial for minimizing accidents. **Conclusion** Improving blasting quality in surface mines requires optimizing blast design, using advanced technologies, and focusing on safety and environmental considerations. By integrating better blast design practices, real-time monitoring, and effective mitigation techniques, mines can enhance productivity, reduce costs, and ensure a safer working environment. Regular training and feedback loops are key to ongoing improvements in blasting operations.

In the context of mining value chain optimization, specifically Mine-to-Mill (M2M), an expensive day in blasting can translate into a highly profitable day for the entire value chain. Now, lets "assume", we do a great job at the operational level and manage to sustain it over the Life-of-Mine (LOM). QUESTION: What impact can poor Strategic (Long-Term) Mine Planning, or 'SMP,' have on an operation's profitability over the Life-of-Mine (LOM)? A poorly developed SMP featuring sub-optimal pit limit, COGs, mining direction, pushbacks and production schedules can result in hundreds of millions of dollars lost over the LOM. This means that decisions about what to mine, how to mine it, and when to mine each block are far more critical to overall value realization than downstream performance in blasting, loading and hauling, milling, or flotation alone. If we fail at the Strategic Mine Planning stage, no matter how much is invested in metallurgical characterization, how well we do Drill & Blast, how intensively Mine-to-Mill optimization campaigns are conducted, or what new technologies are applied to enhance process plant KPIs; it won’t be enough. That is, the value loss resulting from poor upstream strategic decisions can rarely, IF EVER, be fully recovered. That is, an operation can’t optimize its way out of a flawed Strategic Mine Plan and unfortunately all the hard work and downstream investments can be completely undermined by poor LOM decisions. So, we should get the big decisions right first; a flawed mine plan ruins everything downstream.

In 2019, Benchmark’s Simon Moores told the US Senate, “we are in the midst of a global battery arms race.” Critical mineral mining and refining was emphasised as a key battleground in this race, with Moores saying “those that control these supply chains will hold the balance of industrial power for the 21st century in the auto and energy industries.” Especially in critical mineral refining, this balance of power is heavily skewed towards China due to the scale of investment at this critical point in the supply chain. How has China’s dominance evolved since Moores’ speech? Are other countries gaining market share in the upstream? And, is the US still a “bystander” as Moores said in 2019? China gains ground domestically and internationally Critical mineral supply chains are highly-concentrated within China. This trend has only continued since 2018, with China gaining ground in several minerals. For example, China accounted for 12% of global lithium mining in 2019, rising to 21% in 2025 thanks to the development of mica mines. Its share of lithium refining, too, has increased. One area in which domestic production has lost significant market share is nickel refining, in which the country dropped 16 percentage points between 2019 and 2025, partly due to Indonesia banning nickel ore exports. In response, China invested heavily in Indonesia’s nickel sector. In 2018, Indonesia accounted for 13% of refined nickel production. As of 2025, this has risen to 62%, of which 80% is Chinese-owned. China has also invested heavily in cobalt mines in the Democratic Republic of the Congo. Chinese-owned cobalt mines in the DRC account for 50% of global cobalt supply in 2025. Is the US still a bystander? Across battery raw material markets, the US accounts for just 0–2% of global mined and refined supply. However, a US success story can be found in rare earths, a key family of elements for electric vehicle motors and defence applications. In 2018, the US had no mined or refined production of these minerals. However, as of 2025 the US now has an 11% market share in their mining and a 6% share in their refining on a total rare earth oxide basis.

📊 When designing the Final Pit for a Narrow Vein Gold Deposit during PFS, precision is crucial—especially when balancing ore recovery and waste management. The results from our comparison of the Final Pit Design versus Optimized Pitshell show how the AutoPit Design tool ensures optimal outcomes. Here’s what we found: 💪 Complete Recovery of High-Grade Ore: In that example, comparing the Optimised shell evaluation the APD allowed us to recover 100% of the high-grade ore, ensuring maximum profitability from the richest parts of the deposit. 📈 Increased Total Ore by 2%: Thanks to AutoPit’s optimized design process, we captured an additional 2% total ore, enhancing the overall project economics. The increase came at a slight marginal ore grade decrease of 0.02 g/t, a trade-off well worth the gain in ore volume! 🛠️ 5% Incremental Waste Added: While achieving better ore recovery, we also managed an additional 5% waste—a small price to pay for the significant boost in ore and complete high-grade extraction. 🚀 Why Choose AutoPit for Your Gold Projects? Efficiency: AutoPit eliminates the guesswork and manual effort, delivering optimized designs that help maximize ore recovery while managing waste effectively. Accuracy: Tailored designs for even the most complex narrow vein deposits, ensuring precision every step of the way. Profitability: With optimized pit designs, you ensure the highest return on investment by recovering more ore and mitigating waste.

In mining projects, determining the value of ore is based not only on what is extractable, but also on what is economically worth extracting. At this point, two basic concepts come into play: Grade and Cut-Off Grade. 🟢 Grade: It is the proportion of precious metal contained in an ore. This rate; In ore analysis, it is specified in grams per ton (g/t) or percentage (%). Examples: • 4 grams of gold → 4 g/t Au in 1 ton of ore • 1.8% copper → 1.8% Cu in 1 ton of ore 🟠 Cut-Off Grade: It is the minimum metal ratio that the ore must have in order to be put into operation. This value; Metal prices are calculated based on production costs, enrichment yield, and even future economic projections. 🖊️ Simply: • Grade = How much precious metal is in the ore? • Cut-Off Grade = Is that ratio valuable enough to operate? When grade and cut-off grade are evaluated together, it reveals whether the mining operation will be economically sustainable. Therefore, it is imperative that engineers who are responsible for strategic decision-making in mining systematically evaluate laboratory data together with economic parameters. In this series, I not only define technical concepts, but also cover them in plain language, along with their place in the industry. We hope to make small but solid contributions to both students and engineers working in the field...

UC, and its better-looking friend Local UC, provide estimation of G-T curves by SMU into larger panels for recoverable resources at several CoGs. LUC assigns local values based on OK ranking to get spatial quality within a panel. Advantages include probability and mean local grade in areas of wide spaced data (assuming spatial continuity of course). This non-linear method works well for large, continuous and consistent geology such as broad stratigraphic deposits (i.e. iron ore, bauxite, coal, brine, etc.) with wide-spaced data. Pitfalls of UC is that it does not provide local estimates. It will return a G-T but if you don’t know where the good stuff (> CoG) is within that panel, what’s the point? LUC helps but the estimator must be careful because Mother Nature does not like consistency like geostatisticians may assume. One pesky fault, a change in weathering/alteration, or something else will negate the assumptions of stationarity along with poor variography. In early days projects, LUC is fantastic but check for cooperative geology/domains before simply applying this method. Happy Friday!

90% of companies stall before they scale. Not because the market shifted. Not because the team isn’t capable. And definitely not because of a lack of vision. They stall because their operations can’t keep up with their growth. I’ve seen it over and over. The CEO is still making too many decisions. Processes live in people’s heads. Everyone’s working hard, but execution is inconsistent. So the team starts spinning. Firefighting replaces focus. And big goals turn into reactive checklists. When that happens, the fix isn’t more hustle. It’s operational discipline. But here’s the part most people miss: Operational excellence isn’t one big change. It’s a layered process. Built step by step. You start with standardization. Create one clear way to do the work. No more “everyone has their own method.” Then you move to automation. Eliminate repetitive tasks. Free up time for deeper work. Next comes measurement. Track the right numbers. Make them visible. Let the data guide your decisions. Then, layer in continuous improvement. Small weekly fixes. Fast iterations. Constant learning. Only then are you ready for real innovation. Not chaos disguised as creativity. Bold ideas that stick, scale, and move the business forward. This isn’t a theory. It’s how strong, sustainable companies actually scale. From startups to 8-figure teams, the pattern is the same. Build the layers in order. Tighten the engine before you step on the gas.

As a Mine Plan Engineer in the nickel mining, especially in mining contractor, one of my core responsibilities is ensuring that all operational activities to run efficiently, safely, and sustainably. In this discussion session, I collaborated with production team to analyze our fuel ratio. While it may seem like a purely technical metric, this number serves as a key indicator of our equipment's performance and operational efficiency in the pit. To reduce fuel ratio and improve hauling performance, we focused on several measurable indicators, including: ✅ Grade Optimization – Reducing grade of road where possible to minimize engine load. ✅ Rolling Resistance Management – Ensuring regular road maintenance (watering, grading, compacting) to reduce energy loss. ✅ Cycle Time Analysis – Evaluating loading, travel, and dumping times to minimize idle time and improve cycle efficiency. ✅ Payload Management – Monitoring truck loading to avoid underloading or overloading, which both negatively affect fuel usage. ✅ Speed & Gear Utilization – Ensuring proper driving behavior to match road profiles with the most fuel-efficient gear and speed settings. ✅ Refueling & Staging Strategy – Reducing unproductive travel by strategically locating fuel stations and optimizing standby areas. Fuel ratio optimization is not only about cost control, it's also a part of our strategy to support energy sustainability and reduce the carbon footprint of our mining operations. We reviewed historical data, current field conditions, and identified potential areas for improvement. Such as route optimization, cycle time efficiency, and equipment utilization strategies. Cross functional discussions like these are essential. Challenges in the field are rarely solved alone. They require team collaboration, open communication, and a shared understanding of the company’s long term goals. 💡 Mining smarter, not just harder.

If you can’t track it— You can’t lead it: Leadership isn’t about being busy. It’s about being effective. But without the right numbers, you’re managing in the dark. Here’s what happens when you don’t measure: • Goals get misaligned • Progress slows down • Talent gets overlooked • Problems go unnoticed • Decisions lose direction Strong leadership isn’t reactive. It’s informed. It’s deliberate. It’s based on what the data tells you— not what you hope is true. It's using the right metrics to answer hard questions: → Are people engaged—or pulling back? → Are we learning—or just repeating? → Are we working on the right things? → Are we growing—or just surviving? → Are we moving fast enough? Great leaders don’t wait until the end of the quarter to find out something went wrong. They see the signals early, Because the numbers show them where to look. Leaders who measure what matters— Lead what matters.

Te invito a revisar el apunte técnico “Gestión en la Construcción”, una guía clara, estructurada y profundamente útil para quienes buscan fortalecer sus conocimientos en administración de obras y procesos constructivos. 📘 Este documento abarca desde conceptos fundamentales como productividad, costos y plazos, hasta herramientas prácticas como cartas Gantt, curvas “S” y control de avances físicos y financieros. Se destacan temas como: - Planificación estratégica en construcción - Control de gestión y cumplimiento de metas - Identificación de restricciones y toma de decisiones - Comunicación efectiva y liderazgo en terreno 👷‍♂️ Dirigido a estudiantes, profesionales del rubro, este material no solo entrega contenidos técnicos, sino que promueve una visión integral para enfrentar los desafíos de la industria de la construcción actual. 💡 Si estás involucrado en la planificación, supervisión o administración de obras civiles o de edificación, este apunte será una herramienta clave para mejorar tus capacidades y resultados.

=> Key Takeaways - Extracting one ton of gold requires 20,000 MWh of energy, compared to just 250 MWh for silver. - Each ton of gold produces 9,000 tons of CO₂, while silver mining produces only 125 tons. - Mining an ounce of gold costs approximately $1,388, compared to $27 per ounce of silver. At first glance, gold’s higher price per ounce makes it seem like the obvious choice for miners. However, a closer look at production costs and environmental impact reveals why silver remains such an attractive metal to extract. This graphic, sponsored by Vizsla Silver Corp., uses data from Thunder Said Energy, AG Metals, and Metals Focus Gold Mine Cost Service to compare the costs of mining silver vs. gold. ---- => The Energy and Emissions Footprint Gold mining is not only significantly more energy-intensive than silver mining but also produces many more emissions because of the increased energy intensity. Mining Metric Silver Gold Energy Intensity (MWh/ton of metal) 250 20,000 Emissions Intensity (tons of CO2/ton of metal) 125 9,000 Production Cost ($/ounce of metal) 27 1,388 Extracting one ton of gold requires 20,000 MWh of energy, compared to just 250 MWh for silver—an 80-fold difference. This vast energy demand translates into emissions: each ton of gold produces 9,000 tons of CO₂, while silver mining produces only 125 tons. This stark contrast in energy and emissions highlights a key challenge in the gold industry. Despite its high market value, the environmental cost of gold extraction is dramatically higher than that of silver. ---- => Production Costs: Bang for the Buck The financial aspect further supports silver’s appeal. Mining an ounce of gold costs approximately $1,388, compared to $27 per ounce of silver. This means gold is 51 times more expensive to extract than silver when using the all-in-sustaining costs metric. While gold prices are significantly higher, silver’s lower production costs indicate a more efficient return on investment. Silver is a compelling alternative to gold when considering both environmental and financial factors. While gold is valued much higher, silver offers a more sustainable and cost-effective approach to mining. By: Ryan Bellefontaine Graphics & Design: Athul Alexander

We were advancing a feasibility with a vision for a full BEV underground fleet, a true "mine of the future". 90 pieces of gear designed to deliver on ESG, innovation, and long-term value. It was a model case of progressive mine design - but that commitment came with a trade-off. BEVs cost 20-30% more upfront than diesel, and capital was tight - so we took a step back: Trucks and LHDs? High utilization, clear productivity gains - BEVs made sense Drills and support vehicles? Often stationary. Low engine hours. Easy access to power - the business case wasn't there. We proposed a hybrid fleet - BEV where it moved the needle, diesel where it didn't. The result? It saved $25M in upfront capital. The takeaway: Sometimes the future is built in phases, not leaps. Effective fleet strategy balances ambition with practicality - aligning with the levers that matter most. We didn't abandon the future. We made it achievable. How are you approaching major tech shifts in your mine plans?

You have the same 24 hours as a Fortune 500 CEO. So why does it feel like they’re building empires while you’re just clearing your inbox? Because high performers don’t manage time. They defend it. Relentlessly. Executives who run billion-dollar companies. Don't rush. They don’t multitask. They're not constantly checking email. Their calendar? Locked down like Fort Knox. Every hour has a purpose. Here’s what top CEOs do differently: 1. They start with clarity. Every decision flows from priorities. Not preferences. Not emotions. Not other people’s chaos. They wake up knowing what moves the needle. And what just makes noise. 2. They schedule thinking time. Yes, they block space just to think. No meetings. No screens. No fires to put out. Because big problems don’t get solved in reactive mode. They need margin. 3. They make fewer decisions. The more trivial choices you make, the less energy you have for meaningful ones. That’s why they simplify: Same clothes. Same meals. Same routines. Not because they’re boring. Because they’re focused. 4. They don’t do everything. They delegate, automate, or delete anything that doesn’t require them. Because time spent on what only you can do isn’t efficiency, it’s leadership. 5. They protect the 1st hour. Before the world demands their attention, they claim it for themselves. No Slack. No email. No news. Just strategy. Stillness. Or a run at 5 AM. 6. They treat meetings like money. Is it essential? Is it clear? Is it the best use of everyone’s time? If not, cancel it. Or make it an email. Or make it 15 minutes instead of 60. 7. They say no (a lot). Because every yes is a no to something else. Often, something more important. Here’s the truth: Top CEOs aren’t superhuman. They just stop giving their best hours to the wrong things. You don’t need their title. But you can steal their time habits. Try this: • Block time for your most strategic work. • Trim meetings down to half the length. • Say no to things you’d never say yes to if your calendar were empty. You’ll feel uncomfortable at first. But you’ll also feel clarity. And eventually, control. Because time isn’t just what you spend. It’s what you become in the hours you protect.

It wasn’t too long ago when you used to be able to walk into a mine survey office, go over to a plan cabinet, and inspect the mine plans (drawings) which showed excavations of the mine and the parts of the ore body mined out. These plans and sections depicted some of the hazards and proximity of mine workings. Surveyors used to spend are large portion of their time preparing these, with many adding their personal touch of artistry to an ornate North arrow. Computer systems with the various mining software or CAD packages have now taken over from the hand drawing of plans. Go to a survey office today though, and too often you will not be able to see a mine plan of the excavations. Instead, it is more likely the data will be brought up on a screen display, and then be presented with a screen dump or plan depicting a specific area. However, this can only occur if you know where all the relevant data is stored on the computer system and be able to drive the software or, wait until a person such as the Mine Surveyor is available to do this for you. Having plans readily available is not only useful for planning purposes but is especially important for use in emergency situations. Deficiencies in mine plans have also been reported as factors in some mining disasters of the past. For example, Gretley Colliery in NSW in 1996. Even when there is legislation in place, still too often mine plans are not compiled. Is this from a conscious decision to ignore the requirements or a misinterpretation of them? A case in point is the West Australian legislation: The ‘Mines Safety and Inspection Regulations 1995’ stipulate the particulars required in mine plans and when they are to be submitted. However, many fail to read the Regulations in conjunction with the ‘Mines Safety and Inspection Act 1994’ which states that accurate plans of the mine are to be kept up to date. It is worth noting that the onus of procuring and keeping mine plans falls on the manager of the mine. The cost of having in-house or an outsourced compilation of mine plans is not a huge add on to costs already incurred in survey staffing and equipment. So why is it that so many times I see a site that doesn’t have accurate and up to date mine plans? I encourage all Mining Inspectors, Managers and Surveyors to audit this fundamental safety item and act on the proper compilation of mine plans. For helpful reference links regarding this, please see http://www.handebook.com.au/publications/Act%20on%20Mine%20Plans.pdf 25 Aug. 2014

Understanding Geological Structure: The key to drilling efficiency and cost reduction in grade control operations In a surface mine environment, field experimentation makes sure that we understand the geological structure accurately – from dip and strike – completely change the results. At Pansudan Mineral Resources, we integrated geological structure analysis into grade control processes, and saw the difference: 🔍 Precise targeting of ore With the right drilling guidance, we reduced waste and increased extraction efficiency. ♻️ More accurate separation between ore and waste Accurately define structural boundaries and reduce sample interference and increase data quality. 📐 Improved drilling pattern Steering the RC drill perpendicular to the direction of inclination resulted in good coverage with as few pits as possible. ⚠️ Early detection of structural problems Like faults and pleats help us adjust our digging plan early and avoid surprises. 🧭 Reliable geological model Contribute to the connection of every process - from drilling and blasting to production. The bottom line: Every precise structural information with an intelligent production decision = lower cost + better results.

The request for information (RFI) or request for quotation (RFQ) process is an essential part of procurement management in order to make informed decisions about potential suppliers. An RFI is used to gather information about potential suppliers and their capabilities, while an RFQ is a concrete request for quotation (a solution proposal and a price). The first step of the RFI is to identify the company's internal requirements. This includes clarifying the required information, such as specific characteristics, quantities and delivery times. An RFI document is then created that clearly defines the background, specific questions and desired response formats. This is followed by the selection of potential suppliers who are able to provide the required products or services. The RFI is sent to the selected suppliers, who then submit their responses. These are carefully analysed to assess suitability and potential for future collaboration. If necessary, further questions are asked or additional information obtained. Based on the responses received, a shortlist is drawn up with the most relevant suppliers who are eligible for further steps. The suppliers are informed of the result of the RFI and receive feedback or an invitation to the next phase. In the case of an RFQ, the company specifies its requirements and documents in detail the desired goods or services, the conditions, delivery deadlines and evaluation criteria for the selection of offers. The RFQ document is then sent to the previously identified potential suppliers, who are invited to submit a quotation. The bids received are evaluated based on the specified criteria. The supplier that best fulfils the requirements is then selected. If necessary, contract terms, prices or delivery details are negotiated. After the final selection, the contract is awarded to the supplier, formalising the business relationship. Finally, an official purchase order or contract is drawn up, setting out all the relevant terms and conditions. This structured and transparent process enables companies to make informed decisions, minimise risks and ensure efficient collaboration with suppliers. https://www.linkedin.com/in/mariobuesch/

Mining Needs Cultures where Truth Survives In mining, data is everywhere - from fleet telemetry and drill logs to processing plant outputs and tailings dam monitoring. Yet as digital or analytics projects progress, the tipping point often is reached when insights from data do not match with expectations or even prove former decisions wrong. The blame then often falls on the data: "It's incomplete", "It's not ready", "It's biased!". While those critiques may carry some weight - especially at the beginning, when iteration is a mandatory part of the process - the real problem often runs deeper: A culture that rewards comfort over confrontation and hierarchy over honesty. Mines accumulate data at industrial scale, but need to master the challenge to achieve a coherent framework to interpret or act on it - across all organisational levels. This framework or governance should be purely mining driven. It is about mining correlations, process dependencies and business rules. To a large extent findings will confirm assumptions and established practices, but there will be also uncomfortable truths. However, dealing with those truths with comfort (and without filtering them out) is neither a data nor a mining problem. It is about an inclusive and hands-on environment - in short, it is about culture and what creates culture: the people. Beyond the need for mining experts to interpret and connect data, it is essential to involve those directly impacted by the digitalization initiative. Their input is valuable not only for interpreting data related to their specific process steps, but also for engaging them in the journey and fostering transparency about how digitalization might affect their individual roles. Without clear communication and context, many people understandably worry about worst-case scenarios - chief among them, the fear that digitalization will eliminate their jobs. While job displacement may occur in isolated cases, the broader reality is that digitalization often enhances roles: it supports better decision-making, reduces the burden of documentation and enables employees to focus more on meaningful aspects of their work, sometimes even allowing roles to evolve in positive new directions. The significant part of successful digitalization is people-centric (culture!): Actively involving employees in the process and clarifying the range of possible outcomes! Core of this journey is basic groundwork through one-on-one conversations, regular discussions and ongoing engagement beyond. This is not a trivial task, nor is it realistic to expect already fully loaded lead engineers or managers to take it on in addition to their existing responsibilities. These discussions require substance to be meaningful (and not to turn into being counter-productive), with a clear and thoughtful preparation. Typically, this calls for dedicated resources - individuals who can bridge the worlds of mining, IT and human factors - to ensure digitalization in mining is both effective and embraced by all stakeholders. Mining is complex and unforgiving. To thrive, mining companies need more than numbers - they need cultures that can confront reality. That means engagement and empowering technical teams to ask hard questions. It means rewarding those who challenge assumptions with evidence from data. And it means building processes where inconvenient truths aren’t buried but investigated. Only then digitalization and analytics can serve what they’re meant to: clarity, not consensus. Stefan Ebert, Digital Mining Technologist

✅️ How to create Geological ✅️ Database in surpac ✅️ Create Section and Digitize ✅️ Orebody Solid Modeling ✅️ Geostatistics and Variogram ✅️ Block Modeling and Resource Estimation ✅️ Pit Design and Waste Dump Design operations using advanced tools.

Lorsqu’un collaborateur quitte une organisation, son départ est bien plus qu’un simple changement d’effectif. C’est une opportunité unique de recueillir des retours précieux pour améliorer l’environnement de travail, renforcer l’engagement des équipes et optimiser les pratiques managériales. 🔹 Pourquoi l’Entretien de Départ est-il essentiel ? Comprendre les motifs de départ : Identifier les raisons (rémunération, management, évolution professionnelle, etc.) permet d’agir sur les axes d’amélioration prioritaires. Valoriser l’expérience collaborateur : Montrer que chaque voix compte, même à la fin du parcours, renforce la marque employeur. Anticiper les tendances : Analyser les feedbacks aide à prévenir les départs futurs et à adapter la stratégie RH. 🔹 Comment en faire un levier efficace ? ✅ Confidentialité et bienveillance : Créer un climat de confiance pour des retours authentiques. ✅ Questions ciblées : Combiner des questions fermées (évaluations) et ouvertes (commentaires libres) pour une analyse complète. ✅ Plan d’action concret : Transformer les insights en mesures tangibles (formation des managers, ajustement des avantages, etc.). 📢 À tous les professionnels RH et managers : Ne sous-estimez pas la richesse de ces échanges. Un entretien de départ bien mené est un pas de plus vers une organisation plus agile, inclusive et performante !

How We Protect Tunnels and Mines 1-Rock Bolts Steel rods inserted into the rock to bind layers together and prevent separation. 2-Wire Mesh Used with rock bolts to hold small rock fragments and prevent falls. 3-Shotcrete A thin layer of sprayed concrete applied to tunnel walls to strengthen and stabilize them. 4-Steel Sets / Arches Used in areas with weak rock or high stresses to provide strong support. 5-Hydraulic Props Adjustable columns used to temporarily support the roof during operations 🛡️ Why Ground Support Matters: - Protects workers and equipment - Prevents tunnel collapses and production stoppages - Reduces accidents and repair costs Choosing the right support type depends on careful assessment of rock conditions, stress levels, and mine environment. Proper ground support design is essential for the success and sustainability of underground mining.

Las situaciones que pueden comprometer la integridad física de las personas, pueden suceder en cualquier momento y en cualquier lugar. La primera intervención realizada para atender estas situaciones, pueden influir de manera decisiva en mejorar la situación de la persona o personas afectadas Los primeros auxilios son un conjunto de medidas iniciales que se aplican ante una enfermedad o lesión, antes de la llegada de servicios médicos profesionales. La Organización Mundial de la Salud (OMS) subraya que la formación en primeros auxilios no solo salva vidas, sino que también contribuye a reducir el número de incapacidades y discapacidades permanentes en el lugar de trabajo Por esto es necesario que la ciudadanía en general y las empresas conozcan cómo actuar en las situaciones de urgencias más frecuentes y además sepa contactar con los servicios de emergencias Por lo que la correcta aplicación de primeros auxilios puede ser crucial en situaciones de emergencia, sin embargo muchas empresas no se encuentran preparadas para responder adecuadamente. Esta falta de preparación no solo pone en riesgo la salud de los empleados, sino que también puede acarrear implicaciones legales para la empresa. Al respecto adjunto la “Guía de Primeros Auxilios” de Empresas Públicas de Emergencias Sanitarias de España 🇪🇸 Con el objetivo de trasladar conocimientos, de una manera sencilla, haciendo una descripción de las situaciones más frecuentes que se puede encontrar cualquier persona y destacando qué medidas se deben realizar y cuáles no deben hacerse, ya que podrían empeorar la situación. Incluye cómo se debe actuar en situaciones en las que nos podemos ver inmersos como las que ocurren en un entorno inseguro, provocadas por grupos violentos activos. Por lo que la importancia de los primeros auxilios en las empresas es indiscutible. La formación en esta área no solo salva vidas, sino que también reduce los costos asociados a accidentes, promueve una cultura de seguridad y asegura el cumplimiento de la legislación vigente. A medida que las empresas continúan enfrentando desafíos en materia de seguridad y salud, invertir en la capacitación en primeros auxilios debe ser una prioridad estratégica. Al hacerlo, no solo están protegiendo a sus empleados, sino que también están invirtiendo en su propio futuro. Buena lectura !!! Source : Percy Raul Mesias Urday

Yes, the basic knowledge of the interface alone does not suffice . Remember a software is a set of algorithms henceforth garbage in is garbage out . One must possess intricate understanding of the subject matter principles before modeling in software . In my experience as a Mine Planning Engineer i have come across projects that demand a solid knowledge base to tailor a solution otherwise you can make an engineering blunder. A practical example is open pit optimization using Whittle software. There is a common oversight of defining a cut-off grade before running optimisation. Using this approach one would be optimizing ore blocks only . Whereas the optimizer (Lerchs Algorithm) will define for you the economic cut-off grade once you set physical and economical constrains. You must have real-time subject matter understanding coupled with cutting edge software application for effective solutions.

A Mineral Resource Estimate (MRE) isn’t just a number—it’s a technically disciplined, auditable process that transforms geological data into an asset recognized by global reporting codes. 🔷 1.Geological Interpretation Understanding lithology, structure, alteration, & mineralization controls is the first step. 🛠️ It defines domain boundaries & underpins the entire modeling strategy 🔷 2.Data Quantity & Quality Review Drill density, spatial coverage & continuity Sampling recovery, accuracy & QA/QC 📌 Only verified data qualifies for use in compliant models 🔷 3.Data Validation Database cleanup: fix survey errors, depth overlaps, duplicates ✅ A validated dataset is a non-negotiable input for geological modeling 🔷 4.Geological & Grade Domaining 3D modeling of lithological units, oxidation fronts & mineralised envelopes Length-weighted compositing ensures uniform sample support 🔷 5.Grade Capping & Statistical Analysis Remove influence of extreme outliers Analyze grade distributions, CV & normality 🎯 Critical in high-variance or nuggety systems. 🔷 6.Geostatistical Modeling Build variograms per domain Quantify nugget, sill, range & anisotropy 📈 Forms the basis of robust kriging estimates. 🔷 7.QKNA – Quantitative Kriging Neighbourhood Analysis Optimize: Search radius and ellipsoid geometry Block discretization & sample limits 🔍 Ensures estimation precision & conditional bias minimization 🔷 8.Grade Estimation Select method aligned with domain geometry and data behavior: Ordinary Kriging (OK) Inverse Distance Weighting (IDW) Multiple Indicator Kriging (MIK) ⚙️ Methodology must respect domain boundaries and spatial continuity. 🔷 9.Model Validation Swath plots and local/global comparisons Composite vs block validation ✅ Reinforces geological consistency and statistical reliability. 🔷 10.Resource Classification Apply code-compliant criteria (JORC / NI 43-101): Measured – dense data, robust variograms Indicated – moderate support Inferred – wider spaced, lower confidence 📊 Classification underpins project risk, valuation, and disclosure. 🔷 11. Economic Assessment & Cut-Off Application Evaluate Reasonable Prospects for Eventual Economic Extraction (RPEEE): Use recovery, operating cost, and price assumptions Apply block-level cut-off grade logic 📉 Ensures the resource has economic relevance, not just geological presence. 🔷 12. Compliant Mineral Resource Statement Report tonnage, grade, metal content by category. 📝 Signed by a Competent Person (JORC) or Qualified Person (NI 43-101). Transparent. Defensible. Compliant An MRE is where geological confidence, statistical rigor, and economic logic converge. It forms the technical backbone of any mine development strategy, investor decision, or feasibility study.

In the mining industry, the terms "Digitalization" and "Automation" are often used interchangeably, but at their core they represent fundamentally different concepts. Digitalization is about transforming mining processes into data: by collecting, quantifying and intepreting every aspect of operations in numercial form. This approach enables a mathematical and visual understanding of how processes function as it provides critical insights into operational dynamics. The focus is on gaining a deeper, data-driven comprehension of the system. In contrast, Automation to a large extent is about substituting human operators with machines or software, reproducing the actions previously performed by them. This can progress towards fully autonomous operations, where systems function without any human intervention or oversight. Thus, in a nutshell, automation - and in its advanced form, autonomy - usually relies on rule-based configurations. These systems are programmed with predefined instructions and parameters, ensuring that processes are executed consistently and predictably. The key value here is the ability to plan and rely on the process outcomes. Automation is ideal for repetitive, well-understood tasks where the primary goal is to remove variability and enhance safety, gain productivity within clearly defined boundarys and achieve cost efficiency. For example, haul trucks following predefined routes. As mining operations become more autonomous, the reliance on human intervention diminishes, but the underlying logic remains rule-based. Digitalization, on the other hand, is about unlocking new levels of understanding. By collecting vast amounts of process data, mining companies can identify inefficiencies, bottlenecks or previously hidden weaknesses. Advanced analytics, artificial intelligence (AI) and machine learning (ML) can then be applied to simulate a large amount of scenarios, predict outcomes and optimize operations - not just replicate existing ones. This continuous feedback loop enables dynamic improvement, rather than static execution. As a result, Digitalization is targeting for a predictive view into the future and improvement. However, usually a human interpretation on the insights provided and active actions on those findings accordingly is a precondition to generate value. While there are significant interdependencies, in particular, how Digitalization and Automation complement each other in modern mining, each one serves a fundamentally different purpose: Digitalization delivers data-driven insights, while automation focuses on replacing human tasks. For this reason, it is important to consider each approach individually, carefully assessing operational needs and addressing their unique challenges. Both are essential to the future of mining. By clearly understanding their distinct roles and benefits to defined mining problems, unlocking the full potential of digital mining transformation can be highly accelerated.

Leadership is a combination of hard and soft skills. Hard skills focus on the specific tasks of leadership, such as finances, strategic planning, project management, and sermon preparation. These technical skills help you fulfill responsibilities that are unique to the role. But leadership also encompasses soft skills. The soft skills of leadership focus on people, teams, and the qualities that build enduring influence. Soft skills are transferrable from one job to the next and are essential regardless of the context. This edition of Make it Count explores 10 soft skills of leadership and considers ways to master each.

We continue with the series: 💎 "Foundations of a Reliable Geological Database" 📍 Post 3: The Critical Connection Between Files – Structure, Errors, and Control. In mineral exploration, we talk about grades, models, and estimations… but all of that begins with a well-structured geological database. A reliable database is not just a tidy spreadsheet — it's a system of interconnected files, where each part contributes essential geological context. 📁 The 4 Essential Files °Collar: 🔍 Possible errors: coordinates outside the grid, incorrect elevations, out-of-range values. ✅ Control: topographic validation, standard formatting. °Survey: 🔍 Common errors: reversed paths, dips >90° incorrectly recorded, paths not starting from the collar. ✅ Control: logical and geometric consistency, dip/azimuth range, review with 3D viewers. 🧪Assays: Chemical analysis data by interval (from/to and grades). 🔍 Frequent errors: overlapping intervals, gaps, duplicate grades, incorrect units. ✅ Control: continuity checks, positive limits, duplicates, unit conversions. °Lithology: 🔍 Typical errors: inconsistent names, intervals not closing properly, overlapping data. ✅ Control: use of a code dictionary, geologist review, QA/QC validation. ➡️ What connects them? 🔏 Hole_ID is the primary relational key—the unique identifier that links each file. 🗝️ Without this clear relationship, data integrity is lost and the model becomes unreliable. 💣 Common error: records with Hole_IDs not found in the collar, duplicates, or inconsistencies across tables. 🚦 How to maintain integrity? ✔️ Implement automatic cross-table validations ✔️ Standardize nomenclature (e.g., “LIT01” ≠ “LITO01”) ✔️ Use scripts to detect overlaps and structural issues ✔️ Ensure traceability with formal QA/QC (change logs, approvals, versioning) ✔️ Educate the team on each file’s role and associated risks 📌 In Summary 📂 Each file is like a layer of information that, when properly aligned, builds the full geological story. ❕ But if just one layer is misplaced or misclassified, the entire model can fail. 🛠️ Inter-file integrity isn’t just a technical issue—it’s a critical practice to avoid multi-million-dollar mistakes.

The recent resilience in mining stocks has been remarkable. Keep in mind that the mining sector still represents just a small share of the global economy, but that’s probably about to change. Both investors and policymakers are beginning to recognize the strategic importance of these companies in a world where hard assets are more essential than ever. It’s time to revitalize a legacy industry.

They’re every job seeker's biggest worry—because you never really know what’s coming. And some of them? Brutal. – “What’s your biggest weakness?” – “Why should we hire you?” – “Tell me about a time you failed.” These aren’t just questions. They’re tests—of how you think, communicate, and stay calm under pressure. Here’s how to handle them: 👉🏻 Be real and strategic. Show growth, not perfection. 👉🏻 Use the STAR method to structure your answers. 👉🏻 Practice out loud—confidence builds with repetition. 👉🏻 Research the role so your answers are tailored. 👉🏻 Ask smart questions too. Interviews go both ways. Don’t memorize. Prepare with purpose. Want all 60 tough questions with smart answers? That’s dropping next. Follow Surabhi Reddy Reddy for more.

The first 90 days are critical for any CPO, Head or Senior Procurement manager to make an impact (It's also the most commonly asked interview question) I've created this cheat sheet for you. 🎁 The part most people miss in planning for the first 90 days is the importance of splitting into three distinct sections: 30 days: Understand the Business 60 days: Understand the spend landscape 90 days: Deliver an aligned vision The way you do this should be broken down further. The first 30 days - Engage Stakeholders - Establish Requirements - Understand the Business - Learn through internal review Days 30 to 60 - Meet key suppliers - Understand the spend - Learn through External Review - Understand the change opportunity Days 60 to 90 - Align your plan with the business - Deliver a roadmap for success - Create a vision to achieve - Drive a dynamic Procurement plan The end point of this first 90 days has to be an exec sign off session to ensure a.) alignment to the strategy and b.) full top down endorsement If you don't have a plan like this for the first 90 days, you're unlikely to deliver strategic impact in a business. It's the very difference between taking on a tactical Procurement role versus a leadership role. Many struggle to make that step up because they don't have a plan for the delivering impact And because they misunderstand the importance of building the foundations. In the cheat sheet below, I've also included the key tasks that need to be delivered in 30/60 and 90 days. A checklist if you like. Has this helped? ____________________ 📌 Do you want a hi-res PDF of this cheat sheet and all the other ones I release straight to your inbox? I’m Tom Mills Procurement Protagonist®️ I simplify Procucurement. Simply sign up to my free newsletter here: https://procurebites.com/ You’ll get this cheat sheet and other valuable gems for free, released every Monday via the newsletter.

For so many drill and blast professionals, its not the "Drill & Blast" department but rather the "Drill & Blame" department. Where "no news is good news". If there is poor dig or other issues you have half the minesite pointing fingers and making noise. When the blast works well its crickets, while patting the production team on the back for nailing dig productivity. A shoutout for the career professionals that stick with it, that are passionate about D&B and know how much goes into getting it right. And praise to the managers who recognise the importance of good D&B; ensuring their D&B professionals are enabled to success, while being recognised for the good blasts and learning from those that need improvement. What have you seen work well to fix the blame culture, to retain our good people and achieve great D&B ?

En esta oportunidad quiero compartir un capítulo importante del libro Open Pit Mine Planning and Design de W. Hustrulid, M. Kuchta y R. Martin. Los límites del pit definen la extensión máxima económicamente viable de un tajo abierto, considerando el material explotable como los bloques dentro del pit con valor económico positivo (mineral), las restricciones físicas y técnicas que involucran el balance entre costos y beneficios. Para determinar estos límites se utilizan métodos que evalúan la rentabilidad y factibilidad geotécnica de la extracción. El método del cono flotante es una técnica geométrica aplicada en secciones bidimensionales para definir un pit mínimo rentable. Consiste en una figura en forma de cono que “flota” o se desplaza sobre las filas de bloques del modelo, identificando y removiendo bloques con valor positivo que pueden ser minados sin comprometer la rentabilidad. Este proceso se realiza de manera iterativa, recorriendo filas y eliminando bloques hasta que no se pueda extraer más material rentable dentro de la geometría definida. El cono flotante es útil para delimitar un pit inicial y estimar el stripping ratio y la rentabilidad local. Sin embargo, este método tiene limitaciones en cuanto a la optimización global, pues puede perder combinaciones rentables o extender el tajo más allá de su límite óptimo. Para superar las limitaciones del cono flotante, se utiliza el algoritmo de Lerchs & Grossmann (LG), basado en programación dinámica para encontrar el pit abierto óptimo considerando todas las combinaciones de bloques. Este método tiene sus variaciones 2D, 2.5D o 3D, y evalúa un conjunto de bloques con sus valores económicos y restricciones geotécnicas (como ángulos de pendiente y acceso). El algoritmo LG 3D construye un grafo dirigido donde los nodos representan bloques y las aristas las relaciones de extracción, es decir, que un bloque superior debe extraerse antes que uno inferior. Así, se busca el cierre máximo o el conjunto de bloques que maximiza el valor neto total, respetando las condiciones de accesibilidad y estabilidad del pit. Este cierre óptimo representa el límite económico y físico del tajo abierto, garantizando el máximo beneficio neto para la explotación minera. Actualmente, el algoritmo Lerchs & Grossmann en su versión 3D es el método estándar y el que utilizan la mayoría de los softwares mineros comerciales para el diseño y optimización de límites de pit abiertos, gracias a su capacidad para manejar grandes modelos de bloques y aplicar restricciones complejas de forma eficiente.

It's not just luck or concrete — it's rock mass classification, and the Q-System is one of the most trusted tools for it in mining and tunneling. Developed in 1974, the Q-System helps engineers evaluate the quality of rock masses using 6 key parameters: 1️⃣ RQD – Rock Quality Designation 2️⃣ Jn – Joint set number 3️⃣ Jr – Joint roughness 4️⃣ Ja – Joint alteration 5️⃣ Jw – Water inflow 6️⃣ SRF – Stress reduction factor The result? A numerical Q-value that tells you exactly how strong or weak your ground is — and what support it needs. 🔩 Based on this, you can determine: Type of rock support (e.g., bolts, mesh, shotcrete) Support spacing Tunnel stability Q-System = turning geology into engineering decisions.

Drill and blast performance was always hot topic in decades past at underground mines. Today it feels like it has gone out of fashion somewhat, with efficiency KPI’s becoming the focus, especially in BI circles. Are we forgetting just how impactful 1% of overbreak can be in an underground stoping mine? I get it… why chase 1% less overbreak when an improvement consultant has dangled promises of 10% efficiency gains in front of your face? I’ll save the discussion about these slated efficiency gains for another article, but lets really look at what 1% overbreak or underbreak means for a typical stoping operation in 2025. While very hard to define an “average size UG operation” we’ll use for simplicity’s sake one with a yearly revenue of $500M. It’s pretty obvious that for every 1% underbreak, $5M is potentially lost, assuming it is never recovered (sterilised). It’s not that simple of course, but lets leave it there as a bit of a yard stick. 10-15% underbreak would not be considered an uncommon result for an operation mining stopes in the 10kt-100kt range, so we are talking about a lot of lost opportunity here, considering everything has already been spent to access and break the dirt. Overbreak is far more complex to work out the cost of, though it can lead to far worse results than underbreak which has a more direct correlation to loss. For example, over breaking into ore of a comparable quality results a gain right? Well, maybe… Your average grade will be maintained in this instance, as long as you can restrict overbreak to the stope walls with ore contacts, a rarity. The flip side is overbreaking into waste, which can hit doubly hard. The old mantra of “Ore + Waste = More Ore” has some holes when we look at the costs associated. In a mine which is production constrained (constrained by fleet capacity) every tonne of waste overbreak you bring up displaces a tonne of ore. This waste tonne then incurs the cost of haulage, grinding, separation and then takes up space in your tailings dam. In addition, as overbreak starts to artificially increase the amount of tonnes removed from the stope, delays to the sequence start to incur, which can range from un-impactful right through to a real pain in the date! Finally, overbreak leads to larger spans (Hydraulic Radii), and in some cases stope failure. Rarely will this be without impact and in the worst cases, this can destroy the value of a mining front. What could this potential overbreak cost be? We lets say you are operating at a 20% (EBITDA) margin and your overbreak goes from a well controlled 10% to a slightly less controlled 15%. At 10% overbreak, your entire margin comes from the last 18% of the ore you mine. Once you add an additional 5% waste overbreak to this number and displace that metal from your year, margins drops disproportionately to 14.4%. This means you have effectively given up 27% of your free cashflow! Remember, total material mined for the year stays the same in this example, you have simply diluted the year's average grade by mining extra waste instead of panned ore. While this is a simplified example using the basic accounting skills of a mining engineer, it illustrates the point... I’m preaching to the choir here if you are a professional underground drill and blast engineer. The issue in 2025 is that less and less of us are. Instead, engineers take a faster track to a senior role, with great tools making up for lack of learnt expertise (a common theme in my articles). The blame in my opinion does not sit with them however. Engineers will take an interest in whatever the industry holds in high regard. Our senior leadership in the mining space sets the standard, not the engineer in the seat. If D&B KPI’s are rarely discussed, the team is under-resourced, the reward is comparatively low and there is a lack of great coaching, then it makes sense the role will be seen as a stepping onto “more important things”. I believe great D&B expertise is the foundation of every great planning engineer. Furthermore, having the D&B nous to solve stoping challenges and unlock opportunities in a mine schedule, will take a scheduler from good to great. Thankfully, drill and blast is a well understood art, and there are many amazing engineers out there who can coach our newer generation on the fundamentals. We just need tech services and mining managers/superintendents to set a higher standard and provide the resources to coach our newer generations. There are some fantastic companies and resources out there who do exactly this, and you can add Strategic Mine Planning Services to that list. If you would like help to elevate the game of your D&B team quickly and sustainably, please reach out. We love coaching our newer generation and seeing the fast results that come with it for the operations that invest in their people. FREE OFFER! If you would like me to walk you through a case study where average overbreak and underbreak were reduced by almost 5% each, message me and lets line up a chat. It'll cost you nothing and you might get some real value from it.

In mining, a Resource/Reserve model is more than just a technical deliverable—it’s a strategic asset. From the boardroom to the pit, decisions hinge on the accuracy, clarity, and defensibility of that model. A robust geological model underpins resource confidence, guides mine planning, de-risks investment decisions, and, ultimately, protects shareholder value. It's crucial to acknowledge that geological models are built on geostatistical estimation—not certainties. There will always be an element of uncertainty. That’s why transparency in the modelling process and deep technical understanding from geoscientists are critical. When technical professionals articulate assumptions, communicate limitations, and work collaboratively, the model becomes a reliable foundation for informed decision-making. Dr Harry McDougal Parker, a pioneer in geostatistics, emphasised the importance of incorporating local geological controls and mining selectivity into resource models. He developed the use of conditional probability distributions to forecast recoverable resources—an approach still in use today . His work reminds us that embracing uncertainty through rigorous methods leads to better-informed decisions. From a corporate perspective, we look for models that: Clearly communicate uncertainty, not just tonnes and grade Integrate across disciplines, aligning geology with mining, processing, and finance Remain agile and auditable, adapting as new data comes in Tell a story, one that both technical teams and executives can trust and act on A great geological model doesn’t just inform—it empowers. It builds confidence, drives capital decisions, and helps deliver on strategy. FYI- This is a common question I am asked by clients. What is the difference between a Resource and Reserve Model? Resource Model: Represents the estimated quantity and quality of mineralisation in the ground, based on geological evidence and limited sampling. It includes Inferred, Indicated, and Measured categories, reflecting increasing levels of confidence—but it doesn’t yet consider economic viability or mine planning. Reserve Model: A subset of the Resource model that has been assessed as economically mineable, incorporating modifying factors such as mining method, processing, costs, and environmental and legal considerations. Reserves are classified as Probable or Proved, reflecting higher confidence and a clear business case for extraction.

The world’s top 20 mining OEMs generated over $66 billion in mining and metals sales in 2024 — but with geopolitical uncertainty, shifting commodity demand, and the slow roll of electrification, the future looks anything but predictable. From Komatsu, Caterpillar Inc. and Epiroc to rising Chinese players like SANY Group and XCMG Group these companies are facing the biggest industry reset in history — and the race to electrification is on. While Sandvik and Liebherr Group are making strides in battery-electric tech, even leaders like Metso and Hitachi CM admit tariff turbulence and global decoupling could reshape market activity. 📉 With copper demand expected to rise 70% by 2050, but new projects facing delays, OEMs will play a pivotal role in keeping the supply chain moving. View the full article in the comments. A must-read for anyone watching where mining equipment, innovation, and strategy are headed next.

Our third post in the Management of Change in drilling and blasting looks specifically at changes in explosives product selection. We offer four case studies to demonstrate how we can affect these changes to achieve a desired outcome, and provide some tips for implementation. When was the last time you re-evaluated your explosive product selection — and what results did you see? We'd like to hear your experiences or challenges with changing products — what worked (or didn’t) at your site?

Hey all, my latest article is ready. Lifting the curtain on some of my takes on truck and shovel queue visualisation. Please share with your network if you like this one! It really helps me. Thanks Information Alignment for the data capability. 👇 The Levels of Queue Visualization Level 0: Not tracking queue time? Yikes! Level 1: The Numbers Speak Level 2: The Picket Fence Level 3: Downtimes Added Level 4: Bring in the Distro's Level 5: Go nuts lol BOSS LEVEL: Real-time baby! Animated Queue: https://youtu.be/9KWZVW0pDEI?si=TBBbhAJBrQzsUTgT

O uso de explosivos em operações de desmonte de rocha é essencial para diversos setores, como mineração, construção civil e obras de infraestrutura. No entanto, essa prática envolve riscos elevados e exige rigor absoluto na aplicação das normas de segurança. 🔒 Segurança não é opcional — é prioridade. Acidentes envolvendo explosivos podem resultar em perdas humanas, danos ao meio ambiente e grandes prejuízos financeiros. Por isso, investir em boas práticas de manuseio, armazenamento e aplicação é indispensável. 💡 Boas Práticas para Aumentar a Segurança nas operações de Desmonte com Explosivos: 1. Planejamento e Projeto do Desmonte Elaborar um plano de fogo detalhado, considerando a geologia, geometria da bancada e proximidade de estruturas. Realizar estudos prévios de vibração e confinamento, especialmente em áreas sensíveis ou próximas a comunidades. Dimensionar adequadamente os furos, a carga máxima por espera e os espaçamentos, para evitar sobre pressões e projeções perigosas. 2. Transporte e Armazenamento de Explosivos Seguir as normas do Exército Brasileiro (DFPC) para o transporte (escolta, veículos adequados, documentação). Armazenar os explosivos em paiol autorizado, com controle de temperatura, ventilação e segurança física. Manter inventário rigoroso e registros atualizados de entrada, saída e uso dos explosivos. 3. Preparação dos Furos Evitar contaminação com água, verificando o tipo de explosivo adequado (ex: emulsões resistentes à água). Inspecionar furos antes do carregamento, verificando obstruções, colapsos ou instabilidades. Carregar os explosivos com pessoal qualificado, usando ferramentas antiestáticas e EPI adequados. 4. Iniciação e Detonação Estabelecer zonas de segurança e evacuação, com sinalização e barreiras físicas. Fazer testes de continuidade e resistência elétrica dos sistemas de iniciação antes da detonação. Garantir comunicação clara e comando único durante o processo de iniciação. Utilizar sistemas de iniciação confiáveis, como detonadores eletrônicos em desmontes sensíveis. 5. Pós-desmonte Realizar varredura completa para detecção de explosivos não detonados. Avaliar os resultados do desmonte (fragmentação, projeções, vibração) para ajustar planos futuros. Registrar e analisar ocorrências e quase-acidentes, promovendo a melhoria contínua. 6. Capacitação e Cultura de Segurança Capacitar periodicamente os trabalhadores conforme NR-19 e regulamentações do Exército. Reforçar a cultura de segurança e comunicação proativa, incentivando o relato de riscos e falhas. Simular situações de emergência regularmente, garantindo preparo em caso de incidentes. 📌 Conclusão: Promover a cultura da segurança nas operações de desmonte com explosivos não é apenas uma exigência legal, é uma responsabilidade coletiva. O cuidado com cada etapa do processo, desde o planejamento até a execução, é o que garante a proteção das pessoas, do patrimônio e do meio ambiente.

The document presents the Turkish and English equivalents of technical terms employed in resource and reserve classification systems, as well as in mineral exploration and mineral resource and reserve reporting processes. It is intended to promote uniformity and clarity of terminology among professionals within the industry. 📢 Classification of Resources and Reserves & Technical Terms Document Released A document titled “Classification of Resources and Reserves & Technical Terms” has been published to provide detailed information on the classification system and technical terminology incorporated in the UMREK Code, the second edition of which was issued in 2023. The document presents the Turkish and English equivalents of technical terms employed in resource and reserve classification systems, as well as in mineral exploration and mineral resource and reserve reporting processes. It is intended to promote uniformity and clarity of terminology among professionals within the industry. 📢 Kaynak-Rezerv Sınıflandırması ve Teknik Terimler Dokümanı Yayınlandı 2023 yılında ikinci versiyonu yayımlanan UMREK Kodunda yer alan sınıflandırma sistemi ve kullanılan teknik terimler hakkında “Kaynak-Rezerv Sınıflandırması ve Teknik Terimler” dokümanı yayımlanmıştır. Bu doküman, kaynak ve rezerv sınıflandırma sisteminin ve maden arama, maden kaynak ve rezerv raporlama süreçlerinde kullanılan teknik terimlerin Türkçe ve İngilizce karşılıklarını içermekte olup sektör profesyonelleri arasında terminoloji birliği sağlamayı amaçlamaktadır. UMREK Websitesi Link: https://umrek.com.tr/Custom/CustomPage/e4dfa148-1ae8-4bf2-9827-6a5330730a46

Are you letting a few degrees bias your resource estimate? You already know that even a slight misalignment of your search ellipsoid can cause a block to be classified either above or below the cut-off grade, exceeding the confidence band. The search ellipsoid is like our invisible Midas hand that determines which samples influence each block's grade. Its properties fundamentally shape our resource models, yet their optimization often doesn't receive the attention it deserves. To determine its parameters, we primarily use variogram analysis and our geological understanding of the deposit. Let's break down the main critical search ellipsoid properties: ➔ Ellipsoid Radii: These define how far we search for samples in each direction. Ideally, they should align with variogram ranges that capture the spatial continuity of mineralization. ➔ Axis Orientation: The major axis should follow the direction of maximum continuity (typically along strike), with semi-major and minor axes oriented along dip and across the structure. ➔ Sector Division: Dividing the ellipsoid into sectors (octants or quadrants) ensures spatial representativity of samples. ➔ Min/Max Points Per Sector: These parameters balance between having enough data for statistical strength while preventing clustering bias. Using a globally-oriented search ellipsoid when estimating a folded and complex structure almost guarantees error. Instead, consider Dynamic anisotropy to locally adjust your ellipsoid orientation block by block, ensuring it follows the mineralization's true path. As we can adjust the dynamism of our search ellipsoid block by block, I think we should also able to adjust the it's dimension lengths according to the geological zones of our deposit. For instance; in some cases 25 m radii can give the same trust with 50 m radii according to the different zones in our deposit. Common errors I've encountered in projects ➔ Search ellipsoids that ignore geological controls ➔ Over-reliance on default software parameters ➔ Not testing multiple ellipsoid configurations ➔ Using the same ellipsoid dimensions across different domains Dear Colleagues, What search ellipsoid challenges have you faced? Have you tried dynamic Anisotropy ? What parameter selection approaches work best in your experience ? Perhaps, instead of the traditional block modelling approach where we deterministically assign a grade value at the centre of each block, a different methodology could be developed instead of block modelling ? ML algorithms have already started to be applied to block modelling. Even if we continue to use the block modelling concept, could we consider replacing the conventional search ellipsoid which serves as our resource estimation tool with dynamically shaped volumetric shapes that adapt the geometry of data ? Tunc Ozbek, MSc Mining Engineer. https://www.linkedin.com/in/tunc-ozbek-123603128/

A Total Station is a surveying instrument that combines an electronic theodolite (for measuring horizontal and vertical angles) and an Electronic Distance Measurement (EDM) device. It measures angles and distances and converts them into coordinates using geometric equations. 1. Conversion from Polar to Cartesian Coordinates (XYZ) When the instrument measures: Horizontal angle (θ), Vertical angle or elevation angle (α), Slope distance (S), It calculates the coordinates of the unknown point using the following equations: X = X₀ + S × cos(α) × sin(θ) Y = Y₀ + S × cos(α) × cos(θ) Z = Z₀ + S × sin(α) Where: (X₀, Y₀, Z₀) are the coordinates of the instrument (station), θ is the horizontal angle (from a reference direction), α is the vertical angle (from the horizontal plane), S is the slope distance to the point. 2. Horizontal Distance and Vertical Difference Horizontal distance (H) = S × cos(α) Vertical difference (ΔZ) = S × sin(α) 3. Angle Calculation Using the Cosine Rule To find angles in a triangle between three points: cos(θ) = (a² + b² - c²) / (2ab) 4. 3D Distance Between Two Points To calculate the spatial distance between two points: D = √[(X₂ - X₁)² + (Y₂ - Y₁)² + (Z₂ - Z₁)²]

La OIT destaca la importancia del manejo adecuado de los explosivos o detonadores en las minas a cielo abierto, requiriendo la autorización de la autoridad competente para cualquier actividad. Esto incluye el transporte, fabricación, procesamiento, posesión, uso, preparación, tratamiento, despacho, almacenamiento, embalaje, venta, suministro, neutralización, abandono, destrucción y eliminación de explosivos. En una mina a cielo abierto, sólo se deben utilizar explosivos o detonadores aprobados, y la legislación nacional define el término "explosivo" y especifica sus condiciones. El almacenamiento de explosivos debe construirse de acuerdo con las normas impuestas por la autoridad competente, quien será la responsable de la construcción. No se debe permitir que se almacenen artículos en el mismo polvorín que otros explosivos. El área que rodea el polvorín debe estar limpia, seca y ventilada, con paredes y pisos que mantengan buenas condiciones y seguridad. Los explosivos deteriorados deben aislarse y destruirse de acuerdo con las instrucciones del fabricante. Cuando se interrumpan o suspendan las operaciones, todos los explosivos deben transportarse a un lugar seguro o destruirse de manera aprobada. La empresa es responsable de preparar los procedimientos para el transporte regular de todos los explosivos y detonadores. Los vehículos utilizados para el transporte de explosivos deben cumplir con ciertos requisitos, como tener una construcción robusta, aberturas adecuadas, sistemas de extintores y un conmutador para aislarlos. Se debe especificar la zona de peligro en las minas a cielo abierto y los procedimientos para la protección de las personas y bienes afectados por explosiones, proyecciones de fragmentos, contaminación y humos. Las sustancias inhalables pueden verse afectadas por la exposición a agentes tóxicos, como el daño al tejido pulmonar, la dispersión de neutrones y los gases que agotan el oxígeno. Las altas concentraciones de ciertos asfixiantes pueden causar cáncer de pulmón, mientras que los altos niveles de contaminación en los puntos de carga, los puntos de transferencia de materiales y los sitios de eliminación de desechos pueden reducir la visibilidad de los trabajadores. La evaluación de riesgos debe comenzar con una evaluación de riesgos exhaustiva, considerando factores como la naturaleza de la operación, los peligros potenciales y las consecuencias potenciales de la operación.

When dealing with post-blast scenarios, it's crucial to be aware of the potential dangers from the blast fumes generate. Here's a breakdown of the key components: - **Carbon Monoxide (CO):** A silent threat, this colorless, odorless gas can lead to severe symptoms like headaches, dizziness, and in extreme cases, unconsciousness or even death. Particularly hazardous in underground settings. - **Nitrous Oxides (NOX):** Sporting a reddish-brown hue, these compounds can trigger irritation and discomfort. Even small doses inhaled amidst blasting activities can prove fatal. - **Carbon Dioxide (CO2):** This odorless byproduct arises from burning carbon and organic substances, as well as respiration. Explosives inevitably generate CO and CO2, especially those containing organic carbon. - **Ammonia (NH3):** With its pungent smell, ammonia has the potential to irritate the skin, eyes, nose, throat, and lungs upon exposure. - The explosive's "oxygen balance" plays a pivotal role in determining the nature and volume of fumes produced. While an oxygen surplus typically yields nitrogen oxides, an oxygen deficit can result in the formation of toxic carbon monoxide. - Improperly primed explosives, water-damaged charges, or those below the critical diameter threshold can also give rise to toxic fumes. - Post-blast safety tip: Always allow ample time for fumes to dissipate before inspecting the area after blasting. Remember, each kilogram of explosives translates to a staggering 1000 liters of potentially hazardous fumes. Stay vigilant and prioritize safety in every blasting operation. 👍 Pic: Example Post Blast fumes in Underground and Open Pit Mining

There’s nothing prouder for a planner than watching the result come together after months of designing, planning, and coordination. During my time as a Mine Planning Engineer at an open-pit gold mine in Indonesia, I was responsible for planning and coordinating operations in an extremely challenging environment, where geothermal activity caused hot groundwater to emerge from the pit floor and walls, with temperatures reaching up to 95°C. My role involved not only short-term mine planning but also daily collaboration with multiple departments, including Geotechnical, Drill & Blast, Mine Geology, Dewatering, and Operations, to ensure the safe and effective execution of the plan. Close supervision and coordination were essential, especially during pre-start meetings where we reviewed safety concerns, progress updates, and real-time adjustments based on site conditions. One critical factor in open-pit mine planning is the sump sequence, especially in high-rainfall areas like Indonesia. With wet season rainfall reaching up to 400 mm/month and geothermal inflows adding as much as 3,800 m³/hour of hot water, a poor sump plan can quickly turn into operational chaos. Limited space at the pit bottom and ramp constraints mean sump locations must align with long-term mine designs, not just weekly production plans. Misaligned sump sequencing doesn’t just pose safety risks, it can also directly reduce ore recovery below target. These challenges highlight why mine planners must think both strategically and tactically. Strategic thinking helps define the long-term direction, such as production goals, infrastructure layout, and overall pit design. At the same time, tactical decisions, like interim design, sump positioning, digging direction, ramp access, and day-to-day coordination, ensure those long-term plans stay on track under real-world conditions. We must understand “Where are we going?” while also knowing “How do we get there today?” At the end of the day, Strategic and Tactical are not separate mindsets in Mine Planning, they are inseparable. I’m truly grateful for the experience, the chance to be mentored by senior engineers and to work alongside a skilled, professional team. Every project teaches something new, and I’m looking forward to the next challenge.

Mine geologists play a pivotal role in the success of mining projects. Their expertise is not only critical in identifying economically viable mineral deposits, but also in driving sustainable mining practices that ensure long-term resource utilization. The contributions of geologists stretch from the early exploration phase to the development and expansion of mine sites, influencing decisions that affect the financial viability and environmental impact of mining operations.

La importancia de un regadío segmentado en las rampas. Nos permite tener control para el frenado de los caex y a la vez nos ayuda a controlar la polución. Desde la instrucción de nuestros operadores hasta la ejecución en terreno, necesitamos ser consistentes en lo que hablamos y en lo que hacemos, siempre enfocados en generar las mejores condiciones en seguridad para operar y lograr nuestras metas. Confirmar nuestros procesos es clave para generar una cultura de mejoramiento continuo, vamos por más, siempre más.

Navigating Precision in Drone Mapping 🌐✈️ Embarking on a mapping mission? 🗺️ Let's explore the pros and cons of three pivotal technologies: Ground Control Points (GCP), Real-Time Kinematics (RTK), and Post-Processed Kinematics (PPK). 🛰️💡 Ground Control Points (GCP): Pros: 1-Absolute Accuracy: GCPs provide a benchmark for absolute accuracy, ensuring reliable georeferencing. 2-Versatility: Suitable for various mapping applications and projects of different scales. 3-Cost-Effective: Initial setup costs might be lower compared to some RTK/PPK solutions. Cons: 1-Time-Consuming: Manual placement and surveying of GCPs can be time-intensive. 2-Logistical Challenges: Accessibility to GCP locations may pose logistical challenges in remote or rugged terrains. 3-Dependency on Surveyor Expertise: Accuracy heavily depends on the surveyor's expertise in placing GCPs. Real-Time Kinematics (RTK): Pros: 1-Real-Time Corrections: Provides real-time, centimeter-level accuracy during drone flights. 2-Reduced Ground Control Needs: Decreases the dependency on a dense network of GCPs. 3-Time Efficiency: Accelerates data collection with instant corrections. Cons: 1-Limited Range: RTK requires a continuous connection to a base station, limiting operational range. 2-Signal Interference: Can be susceptible to signal interruptions in urban canyons or areas with dense vegetation. 3-Cost: RTK-enabled equipment tends to be more expensive than traditional setups. Post-Processed Kinematics (PPK): Pros: 1-Flexibility: Eliminates the need for real-time communication, allowing more flexibility in mission planning. 2-Centimeter-Level Accuracy: Achieves high accuracy through post-processing, comparable to RTK. 3-Reduced Dependency on GCPs: Minimizes the necessity for an extensive GCP network. Cons: 1-Post-Processing Time: Requires additional time for post-processing, impacting real-time decision-making. 2-Equipment Cost: PPK-enabled drones and software may have a higher upfront cost. 3-Learning Curve: Mastery of post-processing workflows may be needed for optimal results. Image credit: ageagle.com You can explore more about world wide RTK services at: RTKdata.com In the dynamic landscape of drone mapping, each technology has its role. Choose wisely based on project requirements, terrain, and budget. Let's elevate our mapping game! 🌐✨

Neural artificial intelligence, particularly neural networks, can have significant implications in resource estimation. These AI models can analyze large datasets and recognize complex patterns that might not be apparent to human analysts. In the context of resource estimation, neural AI can help in: 1. **Data Analysis**: Neural networks can process geological data, such as drill hole data, geophysical data, and geochemical data, to identify valuable patterns and correlations that can improve resource estimation accuracy. 2. **Predictive Modeling**: By training neural networks on historical data, they can predict mineral deposits' locations and characteristics more accurately, helping in estimating resources with higher precision. 3. **Risk Assessment**: Neural AI can assess uncertainties and risks associated with resource estimation by considering various factors and scenarios, providing a more comprehensive understanding of the resource potential. 4. **Efficiency**: Automating resource estimation processes using neural AI can significantly reduce the time and effort required for manual calculations, allowing geologists and engineers to focus on higher-level analyses and decision-making. 5. **Continuous Learning**: Neural networks can continuously learn and adapt to new data, improving their accuracy over time and helping in updating resource estimates based on real-time information. Overall, integrating neural artificial intelligence into resource estimation processes can lead to more accurate, efficient, and data-driven decision-making in the mining and geological industries.Barrick BHP Newmont Corporation Datamine Seequent Artificial Intelligence AngloGold Ashanti GOLD FIELDS Yedidia Mgema MAusIMM Exploration Geologist consultant machine learning enthusiast Smart Mining Resources email;yedidiamgema@outlook.com contact;+255694238804 (WhatsApp) 1st, May, 2025

In tunneling and blasting, safety is non-negotiable, and ventilation plays a crucial role in keeping workers safe and operations efficient. Here’s why effective ventilation is essential for underground projects: 💨 Air Quality Blasting releases harmful gases like CO and NOx. Proper ventilation dilutes these dangerous gases and ensures workers have clean, breathable air. 🌡️ Temperature Control Underground operations can get HOT. Ventilation helps regulate the tunnel's temperature, reducing heat stress and ensuring a comfortable environment for workers. ⚙️ Pressure Management Controlled airflow reduces pressure buildup, maintaining structural integrity and minimizing safety risks during operations. 👁️ Improved Visibility Blasting produces a lot of dust. Effective ventilation clears the air quickly, improving visibility and allowing teams to work more efficiently. ✅ Safety & Compliance Proper ventilation aligns with safety regulations, ensuring compliance while boosting productivity. Regular monitoring of airflow can prevent accidents and enhance operational safety. 💡 As tunneling projects grow in complexity, investing in advanced ventilation systems isn’t just smart—it’s essential for safety and success.

Daily Management System (DMS) or in Japanese Asakai is a structured approach to managing day-to-day operations that focuses on continuous improvement and loss (muda) reduction. Definition of DMS/Asakai : A Daily Management System is a set of practices and tools that enable companies to: 1️⃣ Monitor performance daily 2️⃣ Identify areas for improvement 3️⃣ Take corrective actions quickly 4️⃣ Align KPI’s w/ operational execution 5️⃣ Standardize management processes 6️⃣ Establish standard work 7️⃣ Foster a culture of continuous improvement DMS/Asakai is typically executed through a tiered structure: ➡️ Tier 1 (Team Level): - Daily huddles (5-15 minutes) at the start of each shift - Review of key performance indicators (KPIs) - Discussion of issues and quick problem-solving ➡️ Tier 2 (Supervisory Level): - Daily meetings (15-30 minutes) with team leaders - Review of escalated issues from Tier 1 - Coordination of cross-functional problem-solving ➡️ Tier 3 (Management Level): - Daily meetings (30-45 minutes) with supervisors and managers - Review overall performance and strategic alignment - DMS/Asakai Actions follow up - Review and decision making for escalated issues from Tier 1-2 - Resource allocation and improvement initiatives follow up Key components ✅ Visual management boards displaying KPIs and action items ✅ Standard work for leaders at each level ✅ Gemba walks to observe processes and identify improvement opportunities ✅ Problem-solving methodologies (Root Cause Analysis) ✅ Escalation processes for unresolved issues How DMS/Asakai helps ☑️ Enhances equipment reliability by quickly identifying and addressing maintenance issues ☑️ Improves overall equipment effectiveness (OEE) through daily monitoring and rapid problem-solving ☑️ Reduces unplanned downtime by promoting proactive maintenance ☑️ Increases employee engagement and ownership in continuous improvement efforts ☑️ Aligns daily activities with strategic goals, ensuring focus on critical areas ☑️ Facilitates knowledge sharing and best practice adoption across the organization ☑️ Promotes a culture of accountability and data-driven decision-making By implementing a robust Daily Management System, companies can sustain their performance, driving operational excellence and continuous improvement throughout the enterprise. *Below dash is just an example, details/content may vary based on company metrics/strategic priorities.

Optimizing rock fragmentation depends on understanding how joint fracture systems interact with blasting parameters. Key factors such as joint spacing, dips and orientation play a crucial role in determining fragment size distribution and target optimum powder factor. 𝙅𝙤𝙞𝙣𝙩 𝙎𝙥𝙖𝙘𝙞𝙣𝙜: Closely spaced joints → Smaller, more manageable fragments. Widely spaced joints → Larger blocks, often requiring secondary breakage. J𝐨i𝐧t O𝐫i𝐞n𝐭a𝐭i𝐨n: Influences fracture propagation and overall blast efficiency. 𝐕o𝐥u𝐦e𝐭r𝐢c J𝐨i𝐧t C𝐨u𝐧t (𝐉v): Measures the number of joints per cubic meter. → High Jv : More fractures, easier breakage, finer fragmentation. → Low Jv : Intact rock, larger fragment sizes post-blasting. 𝐑o𝐜k Q𝐮a𝐥i𝐭y D𝐞s𝐢g𝐧a𝐭i𝐨n (𝐑Q𝐃): Evaluates rock integrity and is inversely related to Jv. → Low RQD : Highly fractured rock. → High RQD : More intact, resistant rock mass. 𝑯𝒐𝒘 𝑱𝒗 𝒂𝒏𝒅 𝑹𝑸𝑫 𝑰𝒏𝒇𝒍𝒖𝒆𝒏𝒄𝒆 𝑷𝒐𝒘𝒅𝒆𝒓 𝑭𝒂𝒄𝒕𝒐𝒓 Powder Factor targets are directly affected by jointing characteristics: • 𝐇𝐢𝐠𝐡 𝐉𝐯 / 𝐋𝐨𝐰 𝐑𝐐𝐃: Rock is naturally fragmented, Requiring lower Powder Factor to avoid excessive fines and overbreak. • 𝐋𝐨𝐰 𝐉𝐯 / 𝐇𝐢𝐠𝐡 𝐑𝐐𝐃 : Rock is more intact and resistant, Requing higher Powder Factor to achieve effective fragmentation. Ignoring joint characteristics in blast design can lead to: - Inefficient fragmentation. - Excessive fines or oversized blocks. - Increased costs due to secondary breakage. By integrating Jv, RQD into blast planning, better results can be achieved with optimized costs and improved efficiency.

El KPI olvidado que podría transformar la estrategia de mantenimiento: IM (Índice de Intensidad de Mantenimiento) Dentro de mis años de experiencia sólo una vez escuché sobre este indicador y no precisamente estaba dentro de las métricas de medición de la compañía. En la industria hablamos constantemente de MTBF, MTTR, backlog o disponibilidad. Son indicadores esenciales en mantenimiento, sin duda. Pero hay uno que suele quedar en la sombra y que, bien interpretado, puede revelar verdades profundas sobre nuestra gestión: el Índice de Intensidad de Mantenimiento (IM). ¿Qué es el IM? El IM se calcula como: IM = Horas de mantenimiento / Horas de operación Este indicador refleja cuánto esfuerzo real en mantenimiento (en horas-hombre o de máquina) requiere un activo para mantenerse operativo. Es decir, cuánta "intensidad" debemos aplicar para que el sistema funcione. ¿Por qué es tan valioso? Hace visible lo invisible: Muchos equipos parecen confiables porque no fallan, pero requieren constantes ajustes, limpiezas, calibraciones o intervenciones menores. El IM captura ese esfuerzo que no siempre se refleja en las fallas. Detecta desgaste encubierto: Si un activo aumenta su IM sin fallar más, probablemente está envejeciendo o está siendo sobrecargado silenciosamente. Permite comparaciones más justas: No basta con saber si algo falla o no. Dos líneas de producción pueden tener el mismo MTBF, pero una requiere el doble de horas de mantenimiento. ¿Cuál es más eficiente realmente? ¿Por qué casi nadie lo usa? Porque exige registros detallados y sistemáticos de horas de mantenimiento, algo que no todas las empresas hacen bien. Porque no es glamoroso ni fácil de interpretar si no se contextualiza. Porque aún priorizamos la reacción por sobre la comprensión estructural de los activos. A lo que quiero llegar es que el IM no reemplaza a los grandes KPIs, pero los puede complementar con una mirada crítica y estructural. En un entorno donde buscamos eficiencia, sostenibilidad y optimización, no podemos darnos el lujo de ignorar cuánta intensidad estamos aplicando para mantener nuestro sistema en marcha. Quizás es hora de poner este KPI olvidado en el centro de la conversación. Los escucho.

El mantenimiento reactivo y el mantenimiento proactivo, no son tipos de mantenimiento. Entonces ¿Que son? En el siguiente artículo podrás encontrar la respuesta. El artículo es tomado de mi libro "PLANIFICACIÓN, PROGRAMACIÓN Y COSTOS DEL MANTENIMIENTO" que podrás adquirir en: https://www.bookdelivery.com/fr-en/books/search?q=Jos%C3%A9+Contreras+M%C3%A1rquez

Blank samples comprise reference material with a grade less than the detection limit or should be close to zero, blanks are usually composed of material such as quartz, limestone, granites or basalt or any materials which ensured have grades lower than detection limit. Blanks are very useful in testing samples laboratory preparation and controlling contamination effect, usually it's inserted into sample batch after mineralised samples or main mineralised zones. There are most common two scenarios: * if it was inserted after the meniralised zones and its results recording assays, it should be reviewed the laboratory preparation and the cleanliness. * When it was inserted into expected low grade samples and its results recording high assays, it has been swapped during samples preparation and we can ensure that from our geological logging. It's common that blanks have some assays but it must be very close to the detection limits or until (10) times of it will be acceptable.

¿𝗖𝗢́𝗠𝗢 𝗘𝗦𝗧𝗔́𝗦 𝗘𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗔𝗡𝗗𝗢 𝗟𝗔 𝗘𝗗𝗧 𝗗𝗘 𝗧𝗨 𝗣𝗥𝗢𝗬𝗘𝗖𝗧𝗢? Una EDT (Estructura Desagregada del Trabajo) mal organizada puede complicar todo: la planificación, el control, los reportes y hasta los reclamos. Pero si eliges correctamente su estructura, puedes facilitar la gestión del alcance, los costos y los entregables desde el inicio. En esta infografía te muestro 5 formas efectivas de estructurar la EDT según el tipo de proyecto de construcción: ✔️ Por fases del proyecto (EPC) ✔️ Por áreas físicas ✔️ Por fases específicas ✔️ Por disciplinas técnicas ✔️ Por sistemas constructivos 📌 Cada una tiene su aplicación ideal. La clave está en adaptar la EDT a la lógica real del proyecto, no al revés.

Copper demand is projected to surge 70% by 2050, reaching 50 million tonnes annually. Meeting this demand will require significant investments in mining operations worldwide. Here's an updated look at the world's top copper mines and their recent developments: 1. Escondida (Chile) – 1.28Mt – BHP, Rio Tinto, Mitsubishi/JX BHP just greenlit a $2B optimization project as part of a $10.8B plan. 2. Grasberg (Indonesia) – 816Kt – Freeport McMoRan, PT Mineral Industri Production is back on track after major weather setbacks last year. 3. Collahuasi (Chile) – 559Kt – Glencore, Anglo American, Mitsui Slight dip in output, but solid long-term growth plans are in place. 4. Kamoa-Kakula (DRC) – 437Kt – Ivanhoe, Zijin, DRC gov, Crystal River Achieved near-record output in March, powered by stable energy supply. 5. Buenavista (Mexico) – 433Kt – Southern Copper Expanded forecast for 2024, backed by new projects and upgrades. 6. Cerro Verde (Peru) – 430Kt – Freeport, Buenaventura, Sumitomo Freeport increased its ownership stake mid-2024. 7. Antamina (Peru) – 413Kt – Glencore, BHP, Teck, Mitsubishi Undergoing a $2B extension project to keep production going through 2036. 8. Tenke Fungurume (DRC) – 400Kt – CMOC, Gécamines Capacity now up to 450Kt copper and 37Kt cobalt after 2023 expansion. 9. KGHM Polska Miedź (Poland) – 395Kt – KGHM Delivered 3% YoY production growth and strong financials. 10. Polar Division (Russia) – 345Kt – Norilsk Nickel Output up 6.3% in 2024 with stable operations across key metals. With supply risks and geopolitical pressure building, this list is only getting more relevant. Source: https://www.mining.com/featured-article/ranked-worlds-biggest-copper-mines/

At the mine, we used to think in minutes even bigger like hour, shift, daily. Then we learned to think in seconds after Fleet Management System been implemented. When the FMS breakdown of a 24-minute haul cycle was first seen: 🎯 Waiting = Queuing: 90s 🎯 Spotting: 30s 🎯 Loading: 120s 🎯 Hauling: 675s 🎯 More waiting = Queuing: 15s 🎯 Maneuvering: 15s 🎯 Dumping: 15s 🎯 Returning: 491s Those 15-90 second loss that everyone accepted as normal were almost missed. Each "just 15-90 seconds": 🔻 Cost 1.5 cycles per truck daily 🔻Meant 63 m³ left unmoved every shift 🔻Added up to 37,800 m³ annually per truck Suddenly, seconds weren't so small anymore. We can optimize that using Fleet Management System: 45s saved by optimizing haul routes 15s saved through better spotting 30s reclaimed by eliminating unnecessary stops The Lesson Learned Productivity isn't about heroic efforts - it's about: 🔼Seeing what's invisible (until measured) 🔼Fixing what's "too small to matter" (until it does) The difference between good and great operations isn't measured in hours or minutes - it's counted in seconds that nobody notices... until someone starts counting. cr picture :Chaowasakoo, Patarawan & Seppälä, Heikki & Koivo, Heikki & Zhou, Quan. (2017). Digitalization of mine operations: Scenarios to benefit in real-time truck dispatching. International Journal of Mining Science and Technology. 27. 10.1016/j.ijmst.2017.01.007.

When Neil Armstrong took that first step on the Moon in 1969, over 600 million people watched. It wasn’t just a technological breakthrough but a masterclass in human storytelling. NASA didn’t simply launch astronauts into space. They launched a narrative of courage, innovation, and shared vision. And the world listened. Today I ask myself: Why hasn’t mining, an industry that powers nearly every modern advancement, received the same recognition, curiosity, or admiration? Mining makes the modern world possible: electric vehicles, solar panels, wind turbines, mobile phones, and medical technology. They all rely on mined materials. Yet, the public conversation around mining still lacks inspiration, context, and connection. => We’ve focused on operations, not on storytelling. ------- => A new opportunity for mining engineers: sharing stories grounded in truth For years, mining has been discussed by voices outside the industry, journalists, politicians, marketers, and even lawyers and investors who’ve never stepped onto a site. Some have done well, and others have completely missed the mark. As mining professionals, we know what it’s like to walk into a decline at 4 a.m., lead a crew in 40-degree heat, and juggle safety, performance, and sustainability at the coalface. We’ve seen breakthroughs and breakdowns, the impact on communities, and the transformation in and beyond the mine. That lived experience is powerful – and it’s time we share it. A new space is opening for technical professionals to move into mining marketing and communications, not by abandoning our expertise, but by amplifying it through honest, technical storytelling. => Because people aren’t inspired by data alone – they’re moved by connection. ----- => Mining marketing isn’t a luxury. It’s a strategic priority. Let’s be honest: our industry is facing a trust gap. Community resistance, ESG scrutiny, and a talent shortage are symptoms of a deeper issue: People don’t understand what we do, and we haven’t taken the time to explain it properly. A 2023 report by McKinsey & Co. found that industries with stronger storytelling and public engagement attract more investment and talent. EY’s 2021 findings were clear: mining is still seen as traditional, slow to change, and disconnected from purpose – even as we lead some of the planet's most complex innovation and transformation efforts. And this isn’t just about the general public. Lawyers, regulators, and investors also need stories that help them understand our challenges, vision, values, and risk profiles with greater clarity and trust. We can no longer afford to let others define our narrative. It’s time to invest in mining marketing with the same seriousness as in safety, ESG, and production. ---- => Engineers must lead this change Who better to explain the complexity of decarbonising a fleet, or the strategic importance of critical minerals, than the people doing the work? We need mining engineers who speak both geology and humanity. Who can translate a mine plan into a purpose? Who can explain what’s inside the orebody, but also what’s inside the hearts of the people driving our industry forward? This isn’t about oversimplifying. It’s about building a bridge between the mining world and diverse audiences – communities, students, policymakers, journalists, lawyers, and investors. As someone who’s worked in underground and open-pit mines across Australia and Latin America, I’ve seen the shift that happens when we take ownership of our story, not just the technical version but the human one. ---- => What if mining had its own “Apollo moment”? The Apollo mission united people through science and hope. What if we could do the same for mining? What if our mission was not only to power the energy transition, but also to inspire the next generation, and build trust with those who fund, regulate and advocate for the future of mining? We can, but only if we step out from behind our spreadsheets and start sharing what we do with purpose, clarity, and heart. Let’s talk about the dust. The long swings. The pride of a safe blast. The women changing the face of our industry. The innovation behind block caving. The community now has access to power because of a nearby mine. Let’s talk about what mining truly is. ---- => The road ahead This isn’t a communications trend. It’s a shift in leadership. Mining needs more authentic, technical, relatable voices – people who can speak from lived experience and bring the real story of mining to life. We’re not here to sugarcoat. We’re here to reshape the narrative with strategy, humanity, and truth. So the next time a student, journalist, lawyer or investor asks: “What do miners do?” Let’s make sure they don’t have to guess or Google it. Let’s make sure they hear it directly from us. Just like the moon landing, mining is full of stories worth telling. And maybe–just maybe-the world is ready to listen. 📬 Thanks for reading this edition of Mining Insights. Please share it with someone who needs to hear the real story of mining. ________________________________________ ✧ References: • McKinsey & Company (2023). "Winning in the era of ESG: How mining can build trust." • EY (2021). "Top 10 business risks and opportunities for mining and metals." • NASA Archives (1969). Apollo 11 Mission Report. • Harvard Business Review (2020). "The Science of Storytelling: Why Stories Make Us Human and How to Tell Them Better." • International Council on Mining and Metals (ICMM). Responsible Mining Framework (2022)

An integrated strategic plan for mine technical services should encompass several key components to ensure efficient and sustainable operations. Here are some essential elements: 1. Resource and Reserve Management: Accurate geological modelling and resource estimation. Regular updates to the mineral resource and reserve statements. 2. Mine Planning and Design: Long-term and short-term mine planning. Optimisation of mine design to maximise resource extraction and minimise costs. Incorporation of environmental and safety considerations. 3. Operational Efficiency: Implementation of best practices in drilling, blasting, loading and hauling. Use of technology and automation to improve productivity and reduce operational costs. 4. Risk Management: Identification and mitigation of technical risks. Development of contingency plans for potential operational disruptions. 5. Sustainability and Compliance: Adherence to environmental regulations and standards. Implementation of sustainable mining practices to minimise environmental impact. 6. Stakeholder Engagement: Regular communication with stakeholders, including local communities, government agencies and investors. Transparent reporting on operational performance and sustainability initiatives. 7. Continuous Improvement: Regular review and improvement of processes and practices. Investment in research and development to stay ahead of industry trends and innovations. By integrating these components, mine technical services can align their operations with strategic goals, ensuring long-term success and sustainability. References 1. The role of mine planning in high performance - AusIMM ausimm.com 2. Strategic Mine Planning minemax.com 3. Integrated Strategic Planning for Mining - Whittle Consulting whittleconsulting.com.au

"Veni, vidi, vici." in english: "I came, I saw, I conquered." - Julius Caesar. Carefully observing your blast is an important action you can take to understand what really happened in the ground. Without this critical step you might not ever know what went wrong or how to fix it in your next blast.

1. Con Puntos de Control en Tierra (GCPs). Precisión: Alta precisión absoluta (centimétrica). Uso de GPS RTK/PPK: Complementa o refuerza la precisión. Aplicaciones: Topografía, Catastros, Obras Civiles, Ingeniería. Ventajas: Mayor confianza en la georreferenciación y mejores resultados. Desventajas: Requiere más tiempo en campo y equipo GNSS. 2. Sin Puntos de control en Tierra (Solo GPS del dron) Precisión: Relativa aceptable, pero baja precisión absoluta. Uso del GPS del dron: Dependencia total del GPS embarcado, es decir sin correcciones. Aplicaciones: Agricultura, monitoreo visual, inspecciones rápidas. Ventajas: Más rápido menos equipamiento en campo. Desventajas: Error absoluto puede ser de varios metros depende del GPS del dron. ¿ Por qué es importante esta diferencia? * Proyectos de ingeniería necesitan precisión absoluta para posicionar estructuras con exactitud. * En monitoreo periódico si no hay GPS, los modelos pueden no coincidir espacialmente entre campañas. * El uso del GCPs reduce el error en el procesamiento fotogramétrico (RMS), especialmente en zonas con más señal GNSS.

El cargador frontal CAT 966 es una herramienta esencial en la minería a cielo abierto, desempeñando un papel clave en la carga, transporte y manipulación de materiales pesados. Para garantizar su uso seguro y eficiente, aquí te comparto sus principales funciones, medidas de seguridad y recomendaciones: 💼 **Funciones principales** ✅ **Carga y transporte:** Retira material del frente de carga y lo deposita en camiones o tolvas. ✅ **Excavación y nivelación:** Ideal para mover tierra y nivelar superficies. ✅ **Manipulación de materiales pesados:** Su cuchara robusta permite manejar grandes volúmenes de material de forma eficiente. 🛡️ **Medidas de seguridad imprescindibles** ✔️ **Inspección previa:** Asegúrate de que el equipo esté en óptimas condiciones antes de operar. ✔️ **Visibilidad y iluminación:** Verifica las luces de trabajo y la iluminación del área. ✔️ **Acceso seguro:** Utiliza siempre la técnica de tres puntos de apoyo al subir y bajar del equipo. ✔️ **Evaluación del entorno:** Considera el terreno y las condiciones climáticas antes de iniciar la operación. ✔️ **Mantenimiento preventivo:** Mantén un registro estricto de mantenimiento y detén la operación ante fallas críticas. 📌 **Recomendaciones para una operación eficiente** 🔹 **Capacitación:** Solo personal habilitado debe operar el equipo. 🔹 **Distancias de seguridad:** Respeta los espacios adecuados para prevenir accidentes. 🔹 **Control de fatiga:** Implementa relevos y monitorea el estado físico del operador. 🔹 **Carga adecuada:** No excedas la capacidad máxima y verifica el tamaño del material antes de cargar. 💡 **Consejos clave para operadores** 🔧 Realiza inspecciones previas: Revisa neumáticos, niveles de aceite, frenos y luces antes de comenzar. 🪑 Ajusta el asiento: Mantén una postura cómoda y segura durante la operación. 📦 Evita sobrecargas: Protege el equipo y reduce riesgos. ⚙️ Usa técnicas eficientes: Carga el material de forma uniforme para mejorar la estabilidad. 🚦 Respeta las normas de seguridad: Usa cinturón, controla la velocidad y evita maniobras bruscas. 📚 Capacitación continua: Actualízate con las mejores prácticas y tecnologías aplicadas. 👀 Monitorea el entorno: Mantén una visión clara del área de trabajo y comunica tus movimientos. El cargador frontal CAT 966 no solo es una herramienta de gran capacidad, sino también una pieza que requiere habilidad, conocimiento y compromiso con la seguridad. ¿Qué otras prácticas agregarías para optimizar su operación?

Many leaders struggle with coaching their people “in the moment” fearing their team members will perceive that they’re being micromanaged, hovered over, or their boss is trying to catch them doing something wrong. Leaders don't always see the fine line that exists between "micromanaging" a team member or "working with and helping" one. The ability of a leader to coach their people "in the moment" is not only a desirable trait; today more than ever, it's an essential way to effectively lead a team. What we've learned is that newer generations, now populating the workforce, prefer being coached in real time, rather than having to wait for a one-on-one session or a once-a-year, staged performance review with their manager. Coaching in the moment involves providing immediate feedback, guidance, and support to team members as situations arise. The importance of this real-time coaching can’t be overstated, as it builds on continuous learning and improvement, enhances productivity, and works to develop stronger relationships. Immediate intervention prevents small issues from growing or escalating and helps individuals course-correct before problems becomes larger or bad individual habits form. As a result, teams "coached in the moment" operate more efficiently, meet deadlines and achieve goals with greater ease. In-the-moment coaching shows that their leader cares and is committed to each team member’s success. By taking the time to engage with people in real-time, leaders show they are invested in their people's growth and well-being, a proactive approach that builds trust and opens up communications. From a leader’s perspective, in-the-moment coaching enables them to tailor their guidance to the specific needs and work circumstances of each team member. Coaching of this nature provides contextually relevant feedback where leaders can offer their personal experiences as examples of what they have done in similar situations. These interactions also demonstrate authenticity, letting team members know that challenges and issues are a part of growing on-the-job, experienced by the best of all team members. By consistently providing feedback and recognition in real-time, leaders help team members make habits of the right behaviors and make decisions in line with company values and performance standards. Over time, this ongoing reinforcement encourages a leadership and teamwork mindset, where individuals are inspired to put the organization first and to achieve more as an individual. Coaching-in-the-moment is not about “fixing people” or lurking over people in “search of a crime.” It's practical leadership that appeals to generational workers who will be critical to our success over the next several decades. Do you coach in the moment? Do you do it in ways that make it a consistent part of how you manage and lead others?

This presentation covers various factors impacting development cost estimation, including the impact of assumptions on unit rates and strategies for cost reduction using innovative technologies. Source: https://www.srk.com/en/publications/development-cost-for-miners-understanding-managing-mine-costs

In the March issue of North American Mining magazine, I asked how underground mines can achieve the best in mine ventilation. Key takeaways 👇 Maintaining consistent airflow and climate is an ever-moving target. As mines advance and work deeper, the system resistance increases, reducing volumetric flow if all other things remain equal. Hazardous gases require continuous monitoring and adjustment of ventilation systems to ensure safe levels and allow prompt re-entry after blasting to minimize downtime. Heat generated by mining equipment and mine depth significantly impact the climate. If a hot mine also struggles to maintain airflow, maintaining proper temperatures will be an uphill battle. Mining operations often do not implement a big enough fan to handle growing requirements as the mine advances. When designing a ventilation system, the design should meet maximum needs - and add some. Other factors to consider in ventilation design include mine size and layout, mining method and machinery, worker safety regulations, and the size and placement of ventilation shafts. Technologies like VFDs and geofencing allow mines to tailor the ventilation system for real-time conditions, cutting operating costs, ensuring a safe and comfortable working environment, and reducing a mine’s carbon footprint. Thanks to all my experts for guiding me through this topic: Jonathan Griffith, PE, and Tomas Otterberg at Epiroc Jose Pinedo at Howden, A Chart Industries Company Todd Elswick at Paul's Fan Company Kim Trapani at Stantec Are you interested in reading more like this? The APRIL issue of North American Mining is out now. Check out a link to the magazine in the first comment.

In open pit mines there is often a substantial difference between what is finally produced compared to what was originally planned. This difference is real and expected because our knowledge about the orebody is not complete until it is fully mined. The quality of reconciliation reports depends on the quality (and quantity) of measurements we take throughout the value chain. A good practice is to build numerous measurement points throughout the line of production so that the information can be tracked with confidence. Many numbers used in reconciliation reporting are estimates that come with an associated margin of error. It is important to minimize that margin of error through multiple measurements. Remember the only real number is the money in our bank account. While there are some general publications about reconciliation guidelines, there is no generic guideline for the mining industry. In the author’s opinion, every mine must develop its own site-specific guideline that addresses the complexity of its orebody and the measurement capabilities installed on the site. And remember the reconciliation reporting in a mine is a process that needs time to mature. Source: https://www.srk.com/en/publications/reconciliation-explained-and-solutions-recommended

🔍 La inversión minera sigue siendo la base de la economía peruana, alcanzando en diciembre de 2024 una cifra superior a los US$ 769 millones. A nivel de titulares mineros, los que registraron mayor monto de inversión ejecutado al cierre del 2024 fueron: 🏆 1. Compañía Minera Antamina S.A. 📌 Inversión: US$ 689 millones (13.9% del total) 📌 Clave del éxito: Fuerte inversión en la Unidad Económica Administrativa “Yanacancha 1” y su Planta de Beneficio “Huincush”. 🥈 2. Minera Las Bambas S.A. 📌 Inversión: US$ 385 millones (7.8% del total) 🥉 3. Sociedad Minera Cerro Verde S.A.A. 📌 Inversión: US$ 355 millones (7.2% del total) 🏅 4. Anglo American Quellaveco S.A. 📌 Inversión: US$ 346 millones (7.0% del total) 💡 Dato clave: Estas cuatro empresas en conjunto concentraron el 35.8% de la inversión minera ejecutada a nivel nacional. En este listado, exploramos a las 5 principales empresas según cada rubro de inversión. Conoce su impacto en la industria. 📌 Fuente: Boletín Estadístico Minero – Diciembre 2024 (Ministerio de Energía y Minas

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐂𝐮𝐭-𝐨𝐟𝐟 𝐆𝐫𝐚𝐝𝐞 𝐭𝐨 𝐌𝐚𝐱𝐢𝐦𝐢𝐬𝐞 𝐌𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐟𝐢𝐭𝐬 & 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 – 𝐇𝐨𝐰 𝐈𝐧𝐝𝐢𝐚𝐧 & 𝐆𝐥𝐨𝐛𝐚𝐥 𝐌𝐢𝐧𝐢𝐧𝐠 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐨𝐩𝐭𝐢𝐦𝐢𝐬𝐢𝐧𝐠 𝐢𝐭?? Cut-off Grade is the key to balancing profitability, resource utilization, and sustainability in mining. From Coal India’s opencast mines to NMDC’s iron ore strategy, setting the right cut-off grade ensures long-term success. Adjusting cut-off grades can impact NPV by ±5-16%, depending on production scale and ore type. Smart cut-off strategies mean higher revenues, lower costs, and sustainable operations. Global giants like BHP, Rio Tinto, and Newmont are using AI-driven cut-off grade optimization. Artificial Intelligence & Machine Learning are revolutionizing mining; Real-time grade control, predictive analytics, and automation can boost mine efficiency by 25% and reduce dilution losses by 18%. In Indian Mining scenario, from Hindustan Zinc Limited (HZL) to JSW Steel, mining giants are using dynamic cut-off grade adjustments to stay competitive.

Absolutely! In blasting operations, the sequence of detonating explosives is crucial for achieving optimal fragmentation and displacement of rock. A well-planned blasting sequence can lead to several significant benefits: 1. **Controlled Fragmentation**: By carefully timing the detonation of charges, operators can control the size and distribution of rock fragments. This precision is essential for efficient material handling and processing, ultimately enhancing the overall productivity of the mining operation. 2. **Minimized Ground Vibration**: Employing a sequential blast can significantly reduce the amplitude of ground vibrations. Managing these vibrations is vital for protecting surrounding structures, infrastructure, and ecosystems from potential damage, ensuring compliance with safety regulations. 3. **Improved Displacement**: A thoughtfully executed blasting sequence can optimize the movement of rock. This improvement in displacement not only aids in effective rock removal but also minimizes the risk of fly rock—pieces of rock that are propelled away from the blast site—which can pose hazards to personnel and equipment. 4. **Enhanced Safety**: A meticulously planned sequence enhances safety for workers and machinery by lowering the risk of premature detonations. It ensures that the blast area is secure and that all personnel are at a safe distance during the detonation. 5. **Optimized Loading and Hauling**: Effective fragmentation achieved through a proper sequence facilitates easier loading and hauling of materials. This efficiency leads to smoother operations, reduced downtime, and ultimately contributes to better resource management and cost-effectiveness. In summary, the sequence in the blasting process is a critical factor that influences the effectiveness and safety of the operation. It requires careful planning and execution to maximize productivity while ensuring a secure working environment. By prioritizing the sequence of blasts, mining operations can achieve superior outcomes that benefit both the project and the surrounding community.

Through engaging workbook activities and videos, this Presentation Skills session aims to walk students through the process of how to plan, prepare, practice, and present powerful presentations; students will utilize the information in this workbook every time they need to create a presentation.

1. Optimiser l’exploitation minière • La planification permet de définir la séquence optimale d’extraction (ordre dans lequel on extrait les différentes zones du gisement). • Avec les outils numériques, les ingénieurs peuvent simuler plusieurs scénarios pour maximiser la rentabilité tout en tenant compte des contraintes géotechniques, environnementales et logistiques. • Outils utilisés : logiciels d’optimisation minière et les algorithmes d’optimisation. 2. Planification en temps réel grâce aux données IoT • Grâce aux capteurs déployés partout dans la mine (camions, foreuses, convoyeurs), la planification peut être ajustée en temps réel. • Par exemple, si une machine tombe en panne ou si une zone devient instable, le système propose une replanification dynamique pour éviter les arrêts de production ou les incidents. 3. Réduire les coûts et les temps d’arrêt • Une bonne planification permet de limiter les déplacements inutiles des machines, d’optimiser le transport du minerai, et de réduire l’utilisation d’énergie. • Exemple : la planification 4.0 ajuste les horaires des camions autonomes pour éviter les embouteillages sur les pistes d’accès à la mine. 4. Intégration des contraintes de durabilité • Dans le Mining 4.0, la planification prend aussi en compte des critères environnementaux : réduction de l’empreinte carbone, gestion des déchets, minimisation des impacts sur l’écosystème. • Cela permet de répondre aux exigences de plus en plus fortes en matière de responsabilité sociétale (RSE). 5. Planification multi-échelles (court, moyen et long terme) • Long terme : prévoir la durée de vie de la mine (Life of Mine Planning), les investissements et la stratégie globale d’extraction. • Moyen terme : organiser l’exploitation par phase sur plusieurs mois ou années. • Court terme : ajuster au quotidien les opérations en fonction des conditions du terrain, des performances des machines et des objectifs de production. 6. Simulation et prévision avec l’IA • Grâce à l’intelligence artificielle et au machine learning, les systèmes peuvent simuler l’évolution de la mine et prévoir : • L’évolution des coûts. • La disponibilité des équipements. • Les risques de non-respect des délais. • L’impact d’événements imprévus (panne, météo, conditions géotechniques). 7. Collaboration entre départements • Dans le Mining 4.0, la planification devient un outil centralisé et partagé entre tous les départements : production, sécurité, logistique, environnement. • Les plateformes cloud permettent à chaque service d’accéder aux données en temps réel et de contribuer aux décisions.

In the annual publication "WORLD MINING DATA" production figures of 65 mineral commodities from 168 countries are summarized and statistically evaluated. This information is available in tabular form. Production of mineral commodities is listed in detail by continents, country groups, development status, per capita income, economic blocks, political stability of producing countries, largest producers and others. Production of mineral commodities is also listed by producer countries. World Mining Data thereby provide an indispensable basis for commodity forecasts and activities in minerals policy at national and European level. Source: https://www.world-mining-data.info/

Mine Surveying Notes are crucial for maintaining accurate records of mine workings, essential for safety, efficiency, and profitability, encompassing measurements, calculations, and mapping of the mine's layout Here's a breakdown of what mine survey notes entail: =>Purpose of Mine Survey Notes: Safety: Identifying potential hazards, ensuring safe passage and ventilation, and supporting emergency planning. Efficiency: Optimizing mining operations, planning new workings, and ensuring efficient ore extraction. Profitability: Accurate mapping and resource estimation contribute to better planning and resource utilization, ultimately increasing profitability. Legal Compliance: Providing documentation for regulatory bodies and demonstrating adherence to safety standards. Technical Support: Providing data for mine planning, design, and production(mining), as well as for monitoring and maintenance. =>Key Elements of Mine Survey Notes: - Measurements: Recording distances, elevations, and angles within the mine workings. - Calculations: Determining coordinates, areas, and volumes based on measurements. - Mapping: Creating detailed plans and maps of the mine layout, both underground and surface. - Reporting: Compiling and presenting survey data in a clear and organized manner. - Documentation: Maintaining a comprehensive record of all survey activities, including dates, personnel, and methods used. => Specific Example of Mine Survey Notes: Tunnel Survey: Measuring the length, width, and height of tunnels, as well as their alignment and position. Sources: Merrete surveys ResearchGate

Implicit geological modeling is here to stay. But it’s not the only thing geologists need. Collecting comprehensive sampling and geoscience data from the field is the first step in an exploration or mining company’s quest to define the location, shape, and grade of an orebody. After that, the operation must transform their geoscience information into reliable geological models of the subsurface. Traditional explicit modeling methods do a great job of this, by providing geologists with the tools to visualize and build their unique interpretation of the deposits’ geology down to the smallest detail. Over recent years, implicit geological modeling methods and solutions have spread through the industry and proved they are here to stay, largely in part due to ease of use, speed and intelligence. Whether implicit modeling techniques generate better results than explicit methods remain a topic of discussion. Ultimately, both modeling methods are tools, and like any tool, in the wrong hands, they can be misused. The ability of a geologist to make a model that accurately reflects and represents in-ground conditions is still determined by the skill, knowledge, and due diligence of the individual geologist, combined with adequate field observations and data support, as it should be. For several years now, implicit geological modeling has been gaining ground on the highly trusted and widely used explicit geological modeling methods. There are good reasons for that. The below list outlines some of the clear benefits from implicit modeling. In the below example, the comparison is made between GEOVIA’s Surpac (explicit) and Geology Modeler (implicit) solutions. - Speed – generate complex geological models in minutes. - Automation – build models in an automated way, saving time and making the process auditable and repeatable. - Accessibility – geology model generation is not dependent on a single individual user, anyone within a team can update a setting, or add new data, and update the model. - Traceability – geology model revisions capture and retain modifications over time. This provides geologists with the capability to capture settings, input data, and decisions made for each model generated, which in turn allows for excellent traceability and auditability. This provides geologists with confidence the geology model has been generated using the right data. - Integration – Modern implicit modelling tools can integrate with other platforms used across the business, which improves data security, management, and accessibility within other business processes (such as revision and lifecycle management, task management, issue management, risk management, or project management). In this example, GEOVIA Surpac and the Geology Modeler integrate with Dassault’s 3DEXPERIENCE platform. Explicit models are accurate and reliable. But they are often created using repetitive manual processes; as such they can be difficult to update when new data is supplied. This makes them time-consuming and difficult to work with when responding to changing business objectives or operational demands. It can take hours and sometimes days for a geologist to digitise all their wireframes, create the meshes, make sure those meshes are valid, and then use them in block modeling processes. An implicit geological modeler helps geologists automate that process, which in turn makes the process much faster, as well as fully traceable and repeatable. By reducing the amount of manual work geologists are now doing, an implicit solution gives them more time to spend in the field interpreting the geology or reviewing their data to better understand the genesis and evolution of a deposit. => The benefits of implicit modelling in more detail Radial Basis Function (RBF) Implicit modelling is a process where a continuous mathematical representation of an attribute is determined across a volume. This approach requires minimal interaction from the geologist and the computational calculations drastically reduce the amount of time it takes to perform the modelling. This approach means model generation can typically be completed in seconds or minutes. This is an improvement across orders of magnitude when compared to explicit modeling methods applied to identical data. The Radial Basis Function algorithm (RBF) is well known throughout the mining industry today for its use and adoption in implicit modelling. It has quickly become the go-to algorithm for building implicit geology models. This algorithm defines how each point in 3D space is interpolated based on the cloud of input data points. It is well-known that RBF is well suited to multivariate interpolation with scattered data. It allows us to fit all input source points, and generate a smooth surface, it also works well with a non-uniform set of data points and provides good extrapolation results. There are many implicit geological modelling solutions available based on this approach; however, at GEOVIA we have developed our own algorithms to improve RBF and make it even faster. Data management An accurate and representative geology model is only as good as the data that feeds into it. This reminds me of the phrase ‘rubbish in, rubbish out’, and it’s certainly no different with geology models. Diligent and robust data management and validation practices are essential in ensuring accurate, reliable, and representative geology models. Geologists must manage the mass of geoscience data collected by geoscientists, engineers, consultants, and contractors. This includes drillhole records, geological, and geophysical field observations, geochemical, lithological, and remote sensing data, assays, etc. Many exploration and mining companies today work with explicit modelling software that often uses a file-based approach, where data and files are stored in folders locally on the user’s machine or file servers. This traditional data management tends to be slow, subject to human error, and as a result often causes rework or delays. A business enterprise platform, leveraging a unified data model, either on-cloud or on-premises, provides a secure central data repository where geologists can store geoscience and mining data in any format, structured or unstructured. From there, all geoscience data, along with all other data, such as parametric mine designs, pit optimizations, infrastructure, simulations, land use, etc. – will become part of a unified data model where one data source will always speak to another, and all data is interconnected and relational. Whenever a new piece of data arrives in the repository, it is updated across the platform. The best of both worlds – gaining greater control through harnessing the power of explicit and implicit modelling Implicit modelling methods can also leverage data and benefit from pre-existing explicit geology models to give geologists a better representation of the sub-surface environment. For example, the GEOVIA Geology Modeler can give control back to geologists in the following ways: - Constraint lines – associate or digitise polylines to support the available borehole data and realise desired outcrop patterns or cross-sectional interpretations within an implicit geology model. - Explicit intrusions/orebodies – associate explicit solids to gain explicit control over high-priority domains while still implicitly modeling surrounding geology and structures. - Pinch-outs – globally control the taper of a pinch-outs. - Structural measurements – utilise downhole structural measurements to better model faults or folded deposits. - Dynamic isotropy and trend surfaces – quickly model the changeable geomorphology of orebodies and/or intrusions. These features and more, make for a comprehensive implicit geology modeling approach, which makes it possible for geologists to revise and refine models until they generate the exact model that they need, within an automated and repeatable process. This allows geologists to spend time getting the geological interpretation right, giving them confidence in the resulting implicit model.

Australia and Canada Lead Australia tops the list, with its major mining companies totaling $353 billion in market capitalization. The country is home to two of the biggest miners in the world, BHP and Rio Tinto. Rank Country Company Market Cap (USD) 1 🇦🇺 Australia BHP Group 125B 2 🇨🇳 China China Shenhua Energy 103B 3 🇦🇺 Australia Rio Tinto 97B 4 🇺🇸 U.S. Southern Copper 77B 5 🇺🇸 U.S. Freeport-McMoRan 58B 6 🇨🇭 Switzerland Glencore 55B 7 🇸🇦 Saudi Arabia Maaden 49B 8 🇺🇸 U.S. Newmont 48B 9 🇨🇦 Canada Agnico Eagle Mines 43B 10 🇮🇩 Indonesia Bayan Resources 42B 11 🇲🇽 Mexico Grupo México 39B 12 🇦🇺 Australia Fortescue 36B 13 🇬🇧 UK Anglo American 37B 14 🇧🇷 Brazil Vale 37B 15 🇨🇳 China Zijin Mining 57B 16 🇮🇳 India Coal India 27B 17 🇨🇦 Canada Barrick Gold 28B 18 🇨🇦 Canada Wheaton Precious Metals 27B 19 🇨🇦 Canada Nutrien 26B 20 🇨🇦 Canada Franco-Nevada 24B 21 🇨🇦 Canada Teck Resources 22B 22 🇨🇦 Canada Cameco 22B 23 🇬🇧 UK Antofagasta 21B 24 🇮🇳 India Vedanta 20B 25 🇷🇺 Russia Nornickel 18B 26 🇨🇦 Canada Ivanhoe Mines 15B 27 🇨🇳 China Yanzhou Coal Mining 15B 28 🇨🇦 Canada Kinross Gold 13B 29 🇦🇺 Australia Northern Star 12B 30 🇷🇺 Russia Severstal 11B 31 🇨🇦 Canada First Quantum Minerals 11B 32 🇰🇿 Kazakhstan Kazatomprom 10B 33 🇦🇺 Australia South32 10B 34 🇺🇸 U.S. Royal Gold 9B 35 🇨🇳 China Ganfeng Lithium 9B 36 🇸🇪 Sweden Boliden 8B 37 🇨🇦 Canada Alamos Gold 8B 38 🇨🇦 Canada Pan American Silver 8B 39 🇨🇳 China Tianqi Lithium 7B 40 🇦🇺 Australia Evolution Mining 7B 41 🇨🇦 Canada Lundin Mining 7B 42 🇮🇳 Indonesia NMDC 7B 43 🇨🇦 Canada Lundin Gold 6B 44 🇮🇩 India United Tractors 6B 45 🇯🇵 Japan Sumitomo Metal Mining 6B 46 🇲🇽 Mexico Fresnillo 6B 47 🇵🇱 Poland KGHM 6B 48 🇿🇦 South Africa Harmony Gold 6B 49 🇬🇧 UK Endeavour Mining 5B 50 🇿🇦 South Africa Impala Platinum 5B 🌍 Global Total All Companies Combined 1.4T Rich in minerals, Australia is the world’s largest producer of iron ore, essential for steel production, and lithium, a key component in batteries. While Canada’s economy has struggled to keep pace with its southern neighbor, its mining industry remains formidable. The country’s mining sector has a combined market capitalization of $344 billion, making it the second-largest globally. Canada, the world’s second-largest country by area after Russia, has vast natural resource wealth. It is particularly rich in gold, copper, nickel, and potash. United States with miners totaling $228 billion and China with companies totaling $206 billion come in third and forth, respectively.

This comprehensive map, built from decades of lunar mission data (Apollo-era, LRO, SELENE), provides a standardized view of the Moon’s geology. For mining and geotechnical engineers, it’s an essential tool for quarry site planning and evaluating material availability. Key Insights for Quarry Site Planning 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗤𝘂𝗮𝗿𝗿𝘆 𝗦𝗶𝘁𝗲𝘀 🔸The map highlights geological units like impact melt deposits (Ip) and highland materials (Nc), essential for extracting durable aggregates similar to basalt and granite quarries on Earth. 🔸Areas marked with Ec (ejecta) are typically fragmented and unstable, akin to loose alluvial deposits on Earth—making them less favorable for quarrying. 🔸The map also identifies crater interiors, some of which have solidified melt floors (like Ic2) that could serve as quarry basins with stable material. 𝗤𝘂𝗮𝗿𝗿𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗥𝗼𝗮𝗱 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 🔸On Earth, borrow pits provide materials for roads and embankments. On the Moon, basalt-rich impact melts (Ip) and anorthosite highlands (Nc) can serve a similar purpose, offering crushed stone for road bases, embankments, and structural foundations. 🔸The Geologic Map allows us to identify these resource-rich areas, minimizing excavation efforts and ensuring material consistency. 𝗦𝗶𝘁𝗲 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 🔸 The South Pole region shows numerous craters >20 km in diameter, marked with orange circles. Some of these large craters feature stable, flat floors that are ideal for quarrying, while overlapping or concentric craters indicate weaker, disrupted terrain. 🔸 Evaluating crater stability is crucial, much like assessing the risk of sinkholes or subsidence in terrestrial quarries. The map helps pinpoint areas where multi-impact events may have compromised the ground integrity. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗔𝗰𝗰𝗲𝘀𝘀 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻 🔸 Just as on Earth, reducing haul distances is vital for efficiency. The map’s data helps us plan material transport corridors between quarry sites and construction zones, minimizing energy consumption. 🔸 Identifying gentle slopes and low-elevation pathways helps in planning lunar roads and infrastructure, similar to selecting haul roads in terrestrial quarries. Given the vast experience and well-established practices within the extensive mining industry, adapting these proven methods to the Moon is not only feasible but also manageable. Sources: 1. Lunar/LROC - QuickMap // Orange circles represent craters>20km // Unified Geologic Map labels 2. https://https://lnkd.in/gHbHMbzc 3. Quarry Design Handbook by David Jarvis Associates

Uranium Resources, Production and Demand, also familiarly known as the "Red Book", is a biennial publication produced jointly by the NEA and the IAEA under the auspices of the joint NEA/IAEA Uranium Group. The first edition was published in 1965. Thirty editions have since been published. As the only government-sponsored publication tracking world trends and developments in uranium resources, production and demand, the Red Book is an authoritative source of information on the subject.

Este apunte es una guía concisa y práctica que cubre desde los principios de operación y el análisis geotécnico de macizos rocosos, hasta el diseño estructural de revestimientos y las innovaciones más recientes. Ideal para ingenieros y estudiantes que buscan información precisa, ejemplos claros y recomendaciones de campo.

🔥 𝐓𝐇𝐄 𝐓𝐄𝐌𝐏𝐓𝐀𝐓𝐈𝐎𝐍 Big muckpile. Big pride. It’s the dopamine hit of blast day. But here’s the uncomfortable truth:🔍 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗴𝗮𝗶𝗻 𝗶𝗻 𝘃𝗼𝗹𝘂𝗺𝗲, 𝘄𝗲 𝗼𝗳𝘁𝗲𝗻 𝗹𝗼𝘀𝗲 𝗶𝗻 𝘃𝗮𝗹𝘂𝗲. 🧠 𝐖𝐇𝐘 𝐃𝐎 𝐒𝐎𝐌𝐄 𝐒𝐈𝐓𝐄𝐒 𝐊𝐄𝐄𝐏 𝐆𝐎𝐈𝐍𝐆 𝐁𝐈𝐆? Let’s be honest . it’s not always about the geology or production goals. • 𝗘𝗴𝗼 : Loud blasts look like performance. • 𝗛𝗮𝗯𝗶𝘁 : “We’ve always done it this way.” • 𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺𝗶𝘀𝗺 : KPIs get ticked. Targets met. Meanwhile, operations quietly bleed. Blasting is also psychology . Not just art. Not just science. And sometimes, it’s emotionally driven. 💣 𝐓𝐇𝐄 𝐌𝐈𝐒𝐅𝐈𝐑𝐄 𝐌𝐔𝐋𝐓𝐈𝐏𝐋𝐈𝐄𝐑 Big blasts don’t just concentrate energy. They concentrate 𝗿𝗶𝘀𝗸. One misfire in a small blast? ➡️ Annoying. One misfire in a large blast? ➡️ Dangerous. Disruptive 𝗮𝗻𝗱 Expensive. Let’s break it down: • 👷 𝗦𝗮𝗳𝗲𝘁𝘆 𝗧𝗵𝗿𝗲𝗮𝘁: Unexploded charges = silent killers during mucking or secondary operations. • 🚧 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝗹𝗮𝘆𝘀: Can’t dig. Can’t load. Can’t crush. Everything stops. • 💸 𝗖𝗼𝘀𝘁 𝗦𝗽𝗶𝗸𝗲: Re-drilling, re-priming, idle machines — your “cost-saving” blast just blew its own budget. And ironically? Big blasts often skip quality checks in the name of 𝙚𝒇𝙛𝒊𝙘𝒊𝙚𝒏𝙘𝒚. But when you scale up, your margin for error shrinks. In a small blast, you can’t afford a misfire. In a big one? You can’t afford 𝒂𝙣𝒚𝙩𝒉𝙞𝒏𝙜 𝙡𝒆𝙨𝒔 𝒕𝙝𝒂𝙣 𝙥𝒆𝙧𝒇𝙚𝒄𝙩𝒊𝙤𝒏. And that’s a dangerous bet. 𝐓𝐇𝐄 "𝐄𝐅𝐅𝐈𝐂𝐈𝐄𝐍𝐂𝐘" 𝐎𝐅 𝐁𝐈𝐆𝐆𝐄𝐑 𝐇𝐎𝐋𝐄𝐒 𝐈𝐒 𝐀 𝐌𝐈𝐑𝐀𝐆𝐄 Yes, larger diameter holes = fewer drill meters. Fewer holes sound like savings. But here’s what doesn’t make it into the dashboard: • 💥 𝗨𝗻𝗲𝘃𝗲𝗻 𝗘𝗻𝗲𝗿𝗴𝘆 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻: Large holes don’t spread energy evenly. Fragmentation suffer • 🪨 𝐌𝐨𝐫𝐞 𝐎𝐯𝐞𝐫𝐬𝐢𝐳𝐞: You trade finer fragmentation for unpredictable boulders • 📏 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Wider burdens and spacings = precision out the window. • 🎯 𝐏𝐨𝐨𝐫 𝐓𝐢𝐦𝐢𝐧𝐠 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Fewer holes = less control over initiation sequencing It’s not efficiency. It’s erosion of control, consistency, and downstream productivity. 🧱 𝐁𝐈𝐆 𝐁𝐋𝐀𝐒𝐓𝐒 𝐁𝐔𝐈𝐋𝐃 𝐁𝐀𝐃 𝐇𝐀𝐁𝐈𝐓𝐒 When teams get used to chasing tonnage through size alone, something more dangerous happens: 𝙏𝙝𝙚𝙮 𝙨𝙩𝙤𝙥 𝙤𝙥𝙩𝙞𝙢𝙞𝙯𝙞𝙣𝙜. - Precision gets replaced by routine. - Initiation timing? “Good enough.” - Burden checks? “Next time.” - Post-blast reviews? “No time, we’re busy moving muck.” 🧠 What starts as a tactical decision becomes a cultural drift. And suddenly, a site that once engineered every hole is now managing by momentum Let's stop chasing size and start chasing strategy because “𝘽𝙞𝙜” 𝙤𝙛𝙩𝙚𝙣 𝙢𝙚𝙖𝙣𝙨 𝙪𝙣𝙘𝙚𝙧𝙩𝙖𝙞𝙣.

The VOD concept is one of the key energy-saving principles in underground mine ventilation systems, and it can be applied across all mining commodities. VOD refers to the ability to deliver airflow specifically to the underground areas that require it. In other words, airflow is supplied only to the areas in need, while areas that do not require ventilation receive reduced airflow—or none at all. By implementing this principle, energy consumption from auxiliary fans can be significantly reduced. VOD consists of several levels of control strategies: User Control or Manual Control All ventilation devices—such as main fans, auxiliary fans, dampers, regulators, etc.—are operated manually. Time-Based Scheduling The second control strategy is time-based scheduling, which refers to the concept of triggering different setpoints for fans, regulators, and doors based on time inputs in accordance with a predefined schedule. Activity-Based Control The third strategy is activity-based control, as described by Tran-Valade and Allen (2013). This strategy is defined as the "automatic triggering of pre-defined actions in response to configured events." It can be summarized as an event-triggered function based on alternative inputs other than timers or environmental variables, each of which forms its own category. Similar to time-based scheduling, this control strategy applies the same event-triggering principle but is based on any available variable that can be integrated into the software. Tagging and Tracking This strategy requires the implementation of tag-and-tracking systems, with communication components integrated into the ventilation software. Airflow distribution in the mine is determined based on the location of personnel and equipment, as well as predefined rules for calculating the required airflow for each. Environmental-Based Control The fifth strategy is environmental-based control, which involves automated ventilation system adjustments based on real-time environmental data. This may include various sensor inputs such as gas, dust, diesel particulate matter (DPM), temperature (heat stress), etc., assuming that such sensors can operate underground and provide real-time data. Several components are needed to implement this principle effectively.

When procurement is done right, it leads to: ✅ Smarter spending ✅ Stronger supplier relationships ✅ Seamless operations ✅ Better results But often, inefficiencies creep in: ❌ Delayed approvals ❌ Missed cost-saving opportunities ❌ Supplier conflicts ❌ Repeated errors Here’s how to master procurement: 1️⃣ Need Identification Understand what your organization needs and define clear specifications. KPIs: Percentage of accurately identified needs, number of specification revisions. 2️⃣ Purchase Requisition Formalize internal requests and streamline approvals. KPIs: Requisition approval cycle time, percentage of requisitions approved without revisions. 3️⃣ Supplier Identification Research and shortlist suppliers based on capacity, reliability, and quality. KPIs: Number of qualified suppliers identified, supplier qualification lead time. Etc Website: https://stan.store/Kingslys/p/cargocheatsheets-membership