The biggest mining losses are not always caused by commodity prices. Sometimes they occur one bucket at a time. Even a well-estimated reserve can lose significant value when dilution is not properly controlled. A 10–20% dilution rate can reduce delivered grade, increase processing costs, and negatively impact project economics. This is why successful mining operations integrate: • Geological modelling • Grade control drilling • Ore boundary definition • Selective mining practices • Continuous reconciliation The goal is not simply to mine more tonnes. The goal is to mine the right tonnes. #Mining #OreControl #Dilution #GradeControl #MinePlanning #ResourceEstimation #Geology #OpenPitMining #NickelMining #MiningEngineering #Zvenia

The term "independent peer review" is applied across the resource industry to a wide range of activities, much as "bankable feasibility study" came to describe everything from a desktop screen to a definitive study. What is presented to the board as independent assurance is, on examination, often neither independent of the project sponsor nor delivered by peers in any defensible sense of the term. Three approaches dominate. 1) The internal senior review, in which a major selects two or three of its own engineers who were not assigned to the project and represents the exercise as independent. The reviewers report up the same chain as the sponsor, depend on the same executives for their next assignment, and have an interest in the findings being received as constructive. Only the largest internationals have a bench deep enough to source credibly independent individuals, and even they supplement with externals on material decisions. 2) The management consultant stage gate, structured around a generic framework and conducted by personnel who are intelligent and well presented but who have not held line responsibility for the design, construction or commissioning of a comparable asset. The output measures process compliance. Engineering judgement against the business case is what the board requires, and on that measure the peer test is not met. 3) Engineering verification by another engineering house. Technical competence is adequate, but the firms are direct competitors who exchange staff and bid against each other on subsequent work. The incentive to identify a fundamental flaw in a competitor's study is weak, and the scope is narrow technical assurance rather than business case validation. The methodology to address these gaps was developed during the 1990s with the majors. "Peer" is held to mean best in class in the relevant function, with a demonstrated track record and the personal confidence of the directors and executives, rather than merely senior or available. The business case is at the centre of every review, designed backward from the decision the board has to make on the commitment of shareholder funds. Findings are reported balanced, with positives alongside deltas. All matters of Fact are reconciled, with evidence on the table, before the report is signed, and matters of Opinion in dispute are published within the report, with the project team's position recorded.

Revenue Factor (RF) in open pit optimization is a multiplier applied to the base commodity price to simulate different economic conditions; For example, an RF of 1.0 uses the actual price, while values below or above represent lower or higher price scenarios. When optimization is run at multiple RF values, it generates a series of pit shells that increase in size as RF increases, because more material becomes economically mineable. These shells are called nested pits since each smaller pit lies entirely within the larger ones. Nested pits represent different economic outcomes, allowing Engineers to assess project sensitivity to price changes where large variations indicate higher risk and to identify stable, high-value zones. They are also used to design mining stages (pushbacks), enabling a phased approach that improves cash flow, reduces initial capital requirements, and supports efficient long-term mine planning.

After working on many sites, I keep seeing the same mistakes repeated by young surveyors. Not because they lack talent. Mostly because nobody teaches them the small details that matter. Here are a few common ones: • Not checking control points before starting work • Trusting GNSS accuracy without verifying • Poor tripod setup or leveling • Skipping instrument calibration • Rushing measurements to save time Surveying looks simple from outside. But small mistakes can lead to big problems later in construction. Good surveyors are not just fast. They are careful. They double check. They understand that one wrong coordinate can affect the whole project.

During site investigation, these are the key terminologies that must be logged accurately. 1️⃣ Discontinuity Set Group of joints/faults with similar orientation. Identified via structural mapping and stereonet analysis. 2️⃣ Dip & Dip Direction Orientation of the plane (e.g., 45°/120°). Critical for kinematic analysis in slopes and tunnels. 3️⃣ Spacing Perpendicular distance between joints of the same set. Controls block size and deformability. 4️⃣ Persistence Trace length/continuity of a joint. High persistence = higher large-scale failure potential. 5️⃣ Roughness Surface texture (smooth to very rough). Governs shear strength and dilation (linked to JRC). 6️⃣ Aperture Opening between joint walls. Influences permeability and deformability. 7️⃣ Filling (Infill Material) Clay, calcite, gouge, etc. Often reduces shear strength dramatically. 8️⃣ Seepage Groundwater condition (dry to flowing). Directly affects effective stress and stability. 9️⃣ Wall Strength Strength of intact rock forming joint surfaces. Assessed via hammer tests or point load index. 🔟 Block Size Result of joint spacing and orientation. Controls stand-up time and support requirements.

Hace 4 años, en un proyecto FONDEF entre ALGES y DELPHOS Mine Planning Laboratory, demostramos algo que la industria prefiere ignorar: el modelo determinista tradicional es una simplificación peligrosa. Usamos el supercomputador Leftraru para procesar 1,000 escenarios de simulación geoestadística y 1,000 pits finales. ¿El resultado? Una precisión en la estimación de reservas que el Kriging simplemente no puede alcanzar. La realidad es cruda: Operar con un modelo único es como cruzar un río basándose en la "profundidad promedio". Si el promedio es 1.5 metros, pero hay un pozo de 3 metros, te vas a ahogar. En minería, ese "pozo" se traduce en millones de dólares en pérdidas, planes de producción incumplidos (o estimaciones muy por debajo para asegurar el cumplimiento) y procesamiento de mineral con una variabilidad más alta de la que planeábamos. Tras mi experiencia en Andesite, me di cuenta de algo que subestimé en la academia: El desafío no es sólo el algoritmo, sino también la adopción. ¿será que lo que nos falta no son más datos, sino que es la falta de coraje tecnológico? ¿Por qué seguimos abrazados a modelos que subestiman/ocultan el riesgo? La zona de confort: Es más fácil defender un error si "todos los demás lo cometen de la misma forma". El fetiche de la certeza: Preferimos un número exacto que está equivocado, que un rango de probabilidad que es real. Miedo a la Caja Negra: Si quien toma las decisiones no entiende el modelamiento estocástico, no se firma. Y preferimos la seguridad del pasado sobre la rentabilidad del futuro. Hoy no hay excusas. Ya no necesitas un supercomputador de un centro de Investigación tecnológico; con el poder actual de la nube puedes procesar 100 escenarios en el tiempo que te tomas un café. La tecnología ya no es el cuello de botella. El cuello de botella es el mindset de quienes impulsan su adopción. Abro el debate: ¿Estamos realmente innovando o solo estamos digitalizando procesos obsoletos? ¿Es hora de jubilar el modelo determinista en el largo plazo?

I’m preparing to start a short video series explaining effective mine transportation of materials in mining operations. Before we begin, it’s important to understand some basic factors that influence loading and transportation efficiency: 1️⃣ Bucket Capacity – The maximum volume of material that a loader or excavator bucket can hold, usually measured in cubic meters (m³). It determines how much material can be moved in one loading cycle. 2️⃣ Swelling Factor – The increase in rock or soil volume after blasting or excavation. Broken rock occupies more space than in-situ rock, which affects hauling and storage calculations. 3️⃣ Bucket Fill Factor – The ratio of the actual material loaded in the bucket to its rated capacity. It reflects the efficiency of loading operations and depends on material type and operator skill. Understanding these factors helps improve equipment productivity, haulage planning, and overall mine transportation efficiency. Stay tuned for the upcoming video where I will explain effective mine transportation methods and practical calculations used in mining operations.

Jour 15/30 du Challenge 📘 Fiche Exercice N°15 : Simulation gaussienne conditionnelle – De la génération de champs aléatoires à la représentation de l’incertitude spatiale ✍️ Ing. NGUEYAP AMBANI AUGUSTIN SERGE (Ingénieur Géologue – Spécialiste) Cette fiche présente : 1. Objectif de l’exercice 2. Rappel mathématique fondamental 3. Mise en contexte géoscientifique 4. Données fournies 5. Questions 6. Corrigé détaillé et commenté 7. Transition vers la géostatistique 8. Application pratique (R / Python) 9. Synthèse 10. Remerciements et perspectives 🙏 Merci à toutes celles et ceux qui aiment 👍, commentent 💬 et partagent 🔄 ce contenu. Votre soutien nous permet de continuer à proposer des formations de qualité. 🤝 Nous restons disponibles pour échanger et collaborer avec toute personne intéressée par ces thématiques.