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.

S'il y a un problème commun entre les projets et les mines, c'est bien la question controversée de la dilution minière. La dilution est l'un des facteurs importants qui peuvent avoir un impact significatif sur l'économie d'un projet minier. Cet article cherche à aborder les 4W (What : Quoi) de la dilution : Quoi, Pourquoi, Quoi et Quand. En termes simples, la dilution fait référence aux déchets qui ne sont pas séparés du minerai pendant les étapes de l'extraction et qui sont envoyés à l'usine de traitement (Ebrahimi, 2013).

Grade control is a fundamental aspect of mine operations that ensures the accurate extraction of ore at the desired quality and grade. It involves continuous sampling, data analysis, and decision-making to distinguish ore from waste, preventing dilution and maximizing profitability. The process starts with geological mapping and sampling from blast holes or drill cores, followed by laboratory analysis and statistical interpretation. Accurate grade control helps determine the boundaries of economic ore zones and guides excavation to maintain consistent feed for the processing plant. Modern grade control employs digital mine models, geostatistical estimation, and real-time data integration to enhance decision accuracy. These tools help reduce resource loss, optimize milling operations, and ensure that mine plans align with market and production targets. Effective grade control not only safeguards revenue but also supports sustainable resource utilization by minimizing unnecessary extraction and waste generation. Source: Dominy, S. C., Noppe, M. A., & Annels, A. E. (2002). Errors and Uncertainty in Mineral Resource and Ore Reserve Estimation: The Importance of Getting It Right. Exploration and Mining Geology, 11(1–4), 77–98.

[PT] Resultado de um PoC em escala piloto, o trabalho modela o processo de sinterização para prever o FeO final a partir de mistura de minérios, combustível, fundentes e variáveis de processo. Com cerca de 300 testes, técnicas de aprendizado de máquina geraram um modelo com R² > 0,92, validando a metodologia para controle de qualidade e otimização na siderurgia. Um exemplo sólido de IA aplicada com impacto direto em produtividade. [EN] From a pilot-scale PoC, this paper models the sintering process to predict final FeO using ore blends, fuel, fluxes, and process variables. Across ~300 tests, machine-learning delivered a model with R² > 0.92, validating the approach for quality control and process optimisation in steelmaking. A strong case of applied AI driving measurable productivity gains.

[PT] O estudo demonstra como inferir rapidamente a área específica de sistemas particulados a partir da distribuição granulométrica (parâmetro de “nitidez”) usando modelos Gates–Gaudin–Schuhmann, Gaudin–Meloy e Rosin–Rammler. Os resultados mostram boa aderência estatística, com destaque para Rosin–Rammler quando esta descreve melhor a PSD, oferecendo um atalho útil para controle de processos em que métodos instrumentais seriam lentos ou caros. É uma ferramenta prática para operações de beneficiamento que precisam de respostas rápidas. [EN] This work shows how to quickly infer specific surface area from particle-size distributions via the sharpness parameter using GGS, Gaudin–Meloy, and Rosin–Rammler models. Results indicate strong statistical fit, especially for Rosin–Rammler when it best describes the PSD, providing a practical shortcut for process control where instrumental SSA methods are too slow or costly. A handy tool for mineral processing teams needing fast, data-driven estimates.

[PT] O artigo propõe o uso do método de Monte Carlo para simular padrões geométricos de gabaritos visuais utilizados na avaliação prévia de teores e concentração de fases em campo. A partir de modelagem matemática das fases mineralógicas e de alterações em parâmetros estatísticos que regem a distribuição espacial das fases, obtêm‑se correlações entre os parâmetros e os padrões de superfície simulados. A ferramenta permite estimar rapidamente o teor de blocos de lavra através da análise visual das faces expostas ou apoiar análises petrográficas e metalográficas. [EN] This paper proposes using the Monte Carlo method to simulate geometric patterns of visual templates employed for preliminary assessment of ore grades and phase concentrations. By mathematically modelling mineralogical phases and varying certain parameters of their spatial distribution, correlations are established between these parameters and the resulting surface patterns. The method facilitates rapid estimation of ore grades from visual inspection of exposed bench faces and supports petrographic or metallographic analyses.

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.

Bonjour à tous, Je suis actuellement à la recherche d’une opportunité pour intégrer une équipe sur site minier, spécifiquement dans le domaine du Grade Control. Titulaire d’un BTS en Mine-Géologie-Pétrole, avec des expériences en laboratoire d’analyse minérale (SODEMI) et en sécurité chantier (AIKA Construction), je souhaite aujourd’hui évoluer sur le terrain, en apportant ma rigueur et mes compétences en QA/QC et contrôle qualité des échantillons. Je suis mobile partout en Côte d’Ivoire, motivé, prêt à travailler en rotation et immédiatement disponible. Je serais reconnaissant à toute personne de mon réseau pouvant me mettre en contact avec un chef géologue, un superviseur grade control ou un recruteur dans une compagnie minière. Source : SIKA AGUIEI Salomon 📞 07 79 21 35 27 📧 aguiei.sika@gmail.com 🔗 linkedin.com/in/salomon-sika-61

Every mine plan looks good... on paper. Production targets are met. Budgets approved. Equipment ordered. Everyone feels good until the mine starts underperforming. Month after month. Quarter after quarter. And the excuses pile up: “Unexpected dilution” “Poor ground conditions” “Operational delays” But here’s the truth nobody wants to say out loud: The real failure happened years earlier, when we trusted the orebody model more than we should have. Mining is the only industry I know that builds billion-dollar businesses on statistical guesses... and then gets surprised when reality doesn't cooperate. Geological uncertainty is not a rounding error. It’s not a minor risk. It's shown to be the major contributor to project failures. It’s the foundation your entire operation stands on, or collapses on. And yet, companies build LOM plans assuming the estimated block model is the ground truth. Why? Because it's easier to assume certainty than to quantify uncertainty and plan for it. Because spreadsheets are cleaner when you don’t have multiple scenarios. Because no one wants to explain to the board that the “high-confidence” resource might still let them down. But pretending the orebody is perfect doesn't protect you. It just delays the realization. 🔍 Here’s what actually happens: Resource models, even “measured” ones, have built-in errors, including grade, volume, and continuity errors. Estimation methods like Kriging smooth out the grades, where high-grades (where we make money!) are underestimated, and low-grades are overestimated. Mine plans are optimized assuming every block behaves exactly as estimated. Operations find out the hard way that Mother Nature didn’t read the single 3D model. 🔴 And the cost? Missed production targets. Inability to control contaminants at the plant. Cash flow shortfalls. Poor reconciliation. Erosion of investor trust. Bad CAPEX decisions. Inability to fulfill contracts. All because we decided to ignore the geological uncertainty! ✅ What actually works? Quantify uncertainty, early and often. Simulate multiple orebody realizations that reproduce the local variability under the ground instead of relying on a single “best guess.” Optimize the strategic mine plan looking at all simulations. This will ensure you have integrated risk-management, prioritizing less risky, yet rich, areas early on till more information is available for later project stages. Report the production schedules probabilistically. Mining doesn’t fail because it’s inefficient. It fails because it assumes the earth will behave the way a model says it should. And when that assumption breaks, everything else does too. Maybe it’s time we stop treating geological uncertainty as a technical inconvenience. It’s the core business risk, and facing it in advance is the only way we’ll stop falling short.

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.