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.

Ore dilution and quality control in open-pit mines 1. Definition Dilution in the mining sector refers to the inclusion of sterile material that is not separated from the ore during mining operations and is extracted with the ore. This waste material mixes with the ore and is then sent to the processing plant, resulting in an increase in total tonnage and a decrease in average grade compared with the initial plan. 2. Internal and external dilution Dilution in mining operations can be internal or external. - Internal dilution: occurs inside a mining block in which pockets of waste rock cannot be separated and are extracted with the block; also when there is low-grade material surrounded by high-grade material. - External dilution: refers to waste rock outside the ore body that is extracted within the mining block. It varies according to the geology, the shape of the ore body, drilling and blasting techniques, the scale of operations and the size of the equipment. 3. Impact of dilution on the value of a mine One of the main consequences of dilution is the reduction in mill feed grade. A lower feed grade means lower revenues. For an ore of marginal grade, dilution can reduce the grades to such an extent that it becomes uneconomic to process, in other words, dilution can turn a block of ore into barren. So, as a result of dilution : - There will be a loss of material and the mine's overall reserves will decrease in a given pit. - The energy and materials used in the processing plant to process the waste rock portion of the feed are wasted. By Consequently, the unit operating cost of the crusher increases directly as a function of the dilution factor. 4. Some methods for controlling of ore dilution Dilution control is essential to maximise ore recovery and minimise unnecessary waste rock, which can have a significant impact on the overall profitability of the mine: - Accurate geological modelling and Knowledge of the ore deposit: improvements in the accuracy of geological models and knowledge of the ore deposit are required to accurately delineate the boundaries of ore and waste rock zones ; - Effective grade control: effective grade control procedures must be grade control procedures to accurately determine the grade of ore mined, thereby reducing the risk of dilution due to the mixing of high and low grade materials; - equipment selection and operation: use appropriate mining equipment mining equipment capable of selectively extracting ore without unnecessary dilution; - monitoring and reporting: implement comprehensive monitoring and reporting systems to track dilution levels and identify potential areas for improvement.

Grade control is a crucial step in the gold production chain, aimed at ensuring the quality and profitability of mining operations. However, a number of errors can compromise its effectiveness. Here are the main errors observed in grade control, affecting the entire gold production circuit: 1. Inadequate or unrepresentative sampling Poor sampling can lead to erroneous estimates of gold content. This is particularly problematic in deposits with high heterogeneity or a marked nugget effect. Sampling errors can lead to reconciliation problems between forecasts and actual production. 2. Incorrect geological modeling Geological modeling based on insufficient or poor-quality data can lead to errors in resource and reserve estimates. This affects mine planning and can lead to the mining of uneconomic areas or ore dilution. 3. Excessive ore dilution Dilution occurs when waste material is mixed with ore, reducing the overall grade. This can result from poor delineation of ore blocks, inaccurate excavation or lack of control during loading and transportation. 4. Errors in ore tracking and transportation Poor management of ore transport, such as mixing materials of different grades or dumping in inappropriate deposits, can lead to significant economic losses. Strict adherence to transport and storage procedures is essential to maintain ore quality. 5. Data reconciliation problems Discrepancies between expected grades and those actually produced may arise due to delays in ore processing, process losses or errors in data collection. Such discrepancies make it difficult to assess operating performance and make informed decisions. 6. Lack of staff training and awareness Personnel involved in content control must be properly trained and aware of the importance of their tasks. Lack of training can lead to errors in sampling, modeling or ore tracking, compromising the entire production process. 7. Use of obsolete technologies Failure to invest in modern grade control technologies can limit the accuracy and efficiency of operations. The adoption of advanced monitoring and analysis systems can improve ore management and reduce errors. Conclusion: To ensure efficient and profitable gold production, it is imperative to implement rigorous grade control practices. This includes accurate sampling, reliable geological modeling, efficient management of ore transport, proper training of personnel and adoption of modern technologies.

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.

𝟭-𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗶𝗹𝘂𝘁𝗶𝗼𝗻. Sometimes within a mining block there are waste inclusions or low grade pockets of ore that cannot be separated and are inevitably mined with the mining block. This is called internal dilution. Internal dilution is difficult if not impossible to avoid. The amount of internal dilution varies in different types of deposits. Lithology and grade distribution are important factors in internal dilution. 𝟮- 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗶𝗹𝘂𝘁𝗶𝗼𝗻 External dilution also called contact dilution refers to the waste outside of the ore body that is mined within the mining block. External dilution varies based on geology, shape of ore body, drilling and blasting techniques, scale of operation and equipment size. This is the type of dilution that can be controlled using proper equipment and mining practices. Figure shows a mining block in a bench of an open pit mine with different types of dilutions.

The principle of mine reconciliation is based on the comparison of theoretical and actual data. The aim is to understand the differences, identify the causes and make adjustments to improve future operations. Reconciliation consists of : 1. Comparing estimated and extracted reserves: - Verification of quantities and quality of extracted mineral reserves against initial forecasts. 2. Compare production forecasts with actual production: - Comparison between the quantity of ore forecast for extraction and the quantity actually extracted. 3. Analyze metallurgical yields and recovery rates: - Compare the amount of metal recovered with the forecast, taking into account losses during processing. 4. Analyze dilution and loss factors: - Identify geological and operational factors that can lead to ore dilution and losses in the mining process. Decisions to be made during mine reconciliation: During reconciliation, several strategic decisions need to be made to improve mining, including: 1. Adjustment of reserve estimates: - Revise geological models and reserve estimates based on observed discrepancies between forecasts and reality. This may involve adjusting operating plans and financial forecasts. 2. Modification of extraction methods : - Modify or adapt extraction techniques according to the actual results obtained. For example, if dilution is greater than anticipated, blasting or drilling techniques may need to be reviewed. 3. Metallurgical process optimization : - Improve metallurgical recovery rates by adjusting treatment processes and optimizing equipment utilization. 4. Inventory management : - Adjust inventory and equipment management in line with reconciliation results, ensuring that resources are used efficiently. 5. Review production targets: - Review short-, medium- and long-term production targets and schedules based on actual results. This may lead to an adjustment of financial forecasts and profitability strategies. 6. Improve data quality : - If discrepancies are significant, it may be necessary to review the collection of geological, drilling and metallurgical data in order to improve the quality of information for future reconciliations. To remember: Reconciliation in mining is a crucial step in ensuring the profitability and sustainability of mining projects. Rigorous monitoring of reconciliation can lead to better resource management, more efficient and sustainable operations, and optimized mine performance. We welcome your feedback Source: https://linkedin.com/comm/mynetwork/discovery-see-all?usecase=PEOPLE_FOLLOWS&followMember=goumou

The primary objective of grade control is to maximize profitability while minimizing the risks associated with extracting low-grade ore. The main tasks of grade control in a mining operation are as follows: 1. Sampling and Sample Analysis - Sampling: The control of extracted ore samples is carried out at various stages of the mining process, particularly during ore extraction and excavation. Samples are taken at regular intervals from the mining areas. - Sample Analysis: Samples are analyzed to control the ore quality. 2. Quality and Tonnage Control - Continuous Control: Quality control involves continuously monitoring the quality (grade) and tonnage of the extracted ore. This control is achieved through systematic sampling of extracted ore batches. - Inventory Control: Strict control of ore reserves (piles) for the qualitative and quantitative evaluation of the ore. 3. Geological Modeling and Resource Estimation - Geological Modeling: Modeling of the data obtained for the richest zones and their associated characteristics. - Resource Estimation: Based on samples and drilling data, statistical and geostatistical methods are used to estimate the mineral resources present in the deposit. 4. Mixing Parameter Control - Ore Mixing: Adjusting the ore mix to meet the mill's quality specifications. - Dilution Management: It is important to monitor ore dilution. 5. Mining Plan Monitoring and Optimization - Mining Plan Adaptation: The control grade allows the mining plan to be adjusted based on the results obtained from the samples. - Resource Optimization: Grade control helps identify high-grade areas and plan operations to maximize profitability by extracting the richest ores first. 6. Mine and Process Reconciliation - Data Reconciliation: Once the ore is processed, the results of the processing process are compared with predictions based on grade control data. This reconciliation allows us to verify the accuracy of grade estimates and optimize operating practices. - Process Performance Evaluation: If significant discrepancies are observed between grade data and process results, adjustments may be necessary to improve process efficiency. 7. Communication with Production Teams - Collaboration with Production: The Geologist works closely with the production team to ensure that high-grade areas are extracted according to priority. - Operator training and support: Train operations on the significance and management of extraction. Conclusion Grade control is essential for: - Maximizing the profitability of a mining operation by ensuring that high-grade ore is extracted. - Minimizing costs by avoiding low-grade ore extraction or excessive dilution. - Ensuring that mineral resources are optimally and efficiently exploited. - Ensuring that the processing is as efficient as possible to meet production targets.