Confiabilidad Sin Fronteras: De la Manufactura a la Mina Cómo un sistema de indicadores de mantenimiento y análisis Weibull en Power BI, ya validado en la industria manufacturera, se adapta al transporte y los activos críticos de la operación minera Este artículo presenta la evolución de un sistema de indicadores de mantenimiento y confiabilidad operacional desarrollado en Power BI, originalmente validado en la industria manufacturera y actualmente adaptado al sector minero. Aunque los activos y los entornos operativos son diferentes, tanto una planta de producción como una mina comparten el mismo desafío: tomar decisiones de mantenimiento basadas en datos objetivos que permitan anticipar fallas, reducir tiempos de inactividad y optimizar el desempeño de los equipos críticos. La solución integra en una sola plataforma indicadores clave como disponibilidad, MTBF, MTTR, proactividad del mantenimiento, análisis de criticidad y un módulo avanzado de confiabilidad basado en análisis Weibull. Este último constituye el principal diferenciador del sistema, ya que permite identificar la naturaleza de las fallas de cada activo, distinguiendo entre problemas de mortalidad infantil, fallas aleatorias o desgaste progresivo. De esta manera, las organizaciones pueden definir estrategias de intervención más precisas, evitando acciones correctivas o preventivas ineficaces y reduciendo costos asociados a decisiones mal fundamentadas. La metodología ha demostrado ser altamente transferible entre sectores porque se basa en principios universales de ingeniería de confiabilidad. Parámetros como disponibilidad, mantenibilidad, criticidad y comportamiento de falla son aplicables tanto a equipos de manufactura como a camiones de volteo, excavadoras, perforadoras o sistemas de trituración. La adaptación al transporte minero se concentra principalmente en ajustar la estructura de datos, redefinir criterios de criticidad según el impacto productivo de la operación e incorporar una visión económica más profunda del mantenimiento. Como parte de esta nueva etapa, el sistema incorpora una capa de análisis de costos de mantenimiento que complementa la dimensión técnica y operacional. Esto permite evaluar no solo la confiabilidad de los activos, sino también el impacto económico de cada estrategia de mantenimiento, identificando costos evitables, componentes de mayor incidencia y oportunidades de mejora. El resultado es una herramienta de gestión que reduce significativamente el tiempo dedicado a consolidar información, mejora la calidad de las decisiones y genera beneficios medibles para organizaciones intensivas en activos, tanto en manufactura como en minería. ¿Le interesa conocer más sobre esta adaptación o sobre el sistema aplicado a su operación? Ing. Luis Bastidas +58 412-537-5083

Abstract The Kalemie Territory, located in Tanganyika Province in southeastern Democratic Republic of the Congo (DRC), occupies a geologically and mineralogically significant region at the interface between Precambrian basement domains and the Phanerozoic formations associated with the western branch of the East African Rift System. Despite the economic importance of its mineral resources, geological and mineral-resource information for the territory remains dispersed among regional geological maps, historical geological investigations, and spatial datasets of different origins and scales. This study presents a GIS-based geological and mineral-resource cartographic synthesis of the Kalemie Territory. The resulting map integrates geological units, Precambrian and Phanerozoic terrains, magmatic rocks, tectonic structures, mineral occurrences, hydrographic networks, transportation infrastructure, settlements, and topographic information within a single spatial framework. Mineral occurrences represented on the map include gold, tin, copper, manganese, lead, platinum, coal, and other documented resources. The synthesis also incorporates the Lukuga drainage basin and a topographic representation of the territory as complementary spatial information. The cartographic compilation was produced using a geographic information system and spatial datasets derived from geological and mineral-resource information, topographic and remote-sensing-related sources, and publicly available geographic databases. The final map uses the WGS 84 / UTM Zone 35S coordinate reference system and provides a territorial-scale visualization of the spatial relationships between geology, tectonic structures, drainage, topography, and mineral resources. This work provides a consolidated cartographic reference for the Kalemie Territory and contributes to the organization and visualization of existing geological and mineral-resource information. The resulting map may support geological research, mineral exploration, spatial analysis, territorial planning, and future field-based geological investigations.

REE Exploration Starts With One Question: Where Did the REEs Go? Rare Earth Elements (REE) exploration is not simply about finding high-grade assays. A more fundamental question is: Where did the REEs come from, how were they transported, and where did they ultimately become concentrated? The answer requires us to connect several geological processes: 🔹 Source — What rocks or minerals released the REEs? 🔹 Transport — Were the REEs mobilized by weathering, hydrothermal fluids, or other geological processes? 🔹 Concentration — What geological, mineralogical, or geochemical processes allowed them to accumulate? This is why understanding an REE system requires more than assay data. Geology + Mineralogy + Geochemistry + Exploration Strategy The distribution of REEs within a deposit is controlled by the interaction of these factors. Two areas with similar REE grades may represent completely different geological systems — and therefore require different exploration and evaluation approaches. For a geologist, the objective is not simply to answer: “How much REE is there?” But also: “Why is it there, where did it come from, and how is it distributed?” That understanding becomes critical when moving from exploration data toward geological modelling and resource estimation. This is where the real challenge of REE exploration begins. #RareEarthElements #REE #Geology #MineralExploration #Geochemistry #Mineralogy #ResourceEstimation #GeologicalModeling #Mining #Geologist #ZVENIA

This article presents a paper delivered at the IX International Geological and Geophysical Conference GeoEurasia-2026 (Geological Exploration Technologies: Science and Business), held at MGRI, Moscow, from March 31 to April 4, 2026. The original paper is written in Russian; the summary below is provided in English for accessibility. Integrated Remote Sensing and GIS Analysis for Uranium Exploration: A Case Study of the Falea Deposit, Taoudeni Basin, Mali This study presents a uranium exploration methodology applied to the Falea polymetallic deposit (U-Ag-Cu) in the Taoudeni Basin, western Mali. By combining Landsat-8 OLI satellite imagery, spectral indices, principal component analysis (PCA), and fuzzy logic, the study maps hydrothermal alterations (iron oxides, clay minerals) linked to the structural control of mineralized zones. The resulting prospectivity map confirms known mineralized areas and identifies a new high-potential zone in the northwest of the study area, previously unexplored. This reproducible, low-cost approach can be scaled to other intracratonic basins across Africa. Co-authors: Coulibaly Abdoulaye, N'diaye Abdoulaye Zakiou, Mohammed Ibrahim, Kotelnikov Aleksandr Evgenevich (RUDN University, Moscow).

"From Magnetite to Hematite: Why Iron Ore Mineralogy Matters" In iron ore exploration and mining, Fe grade is often the first parameter we look at. However, "Fe grade alone does not tell the whole story." Two iron ore samples can have relatively similar Fe grades, yet behave very differently during beneficiation. One of the key reasons is "the mineral carrying the Fe", together with its texture, liberation characteristics, and association with gangue minerals. "Magnetite (Fe₃O₄)" has a theoretical Fe content of approximately 72.4% and strong magnetic properties, making it highly responsive to magnetic separation. "Hematite (Fe₂O₃)" has a theoretical Fe content of approximately 69.9%, but is weakly to non-magnetic. Its processing response therefore differs significantly from magnetite, with liberation and mineral associations becoming particularly important. Meanwhile, "goethite [FeO(OH)]" is commonly associated with weathered iron ores. Its structural water can contribute to higher "Loss on Ignition (LOI)", which may influence product quality and processing performance. And then there are the elements that cannot be overlooked: "SiO₂, Al₂O₃, P, and S" These parameters can significantly influence ore quality, beneficiation requirements, processing performance, and ultimately economic value. Therefore, understanding iron ore should go beyond asking: -) How much Fe is present? The more important questions are: -) What mineral is carrying the Fe? -) How is it liberated? -) How will it respond to processing? This is where the integration of "geology, mineralogy, geological modelling, grade modelling, and geometallurgy" becomes critical. A geological model tells us where the ore is. A geometallurgical understanding helps us anticipate "how that ore will behave". Ultimately: "Iron ore is not just a Fe-grade story. It is a mineralogy, liberation, processing, and recovery story." "Before asking how much Fe is in the ore, ask what mineral is carrying the Fe." #IronOre #IronOreGeology #Mineralogy #Geometallurgy #MineralProcessing #GeologicalModelling #ResourceEstimation #MiningGeology #IronOreExploration #Mining

Africa stands at the nexus of geopolitical competition for critical materials that power the energy transition, defense, and digital economies. Combined, Africa holds about one-third of global critical mineral reserves. But it remains burdened by colonial-era geological surveys and infrastructure and exports its bounty largely unprocessed into Chinese-dominated supply chains. Middle powers like the United Arab Emirates (UAE) have emerged as significant players in mining investment on the continent, while the United States and its allies have sharpened their interest in African mineral markets. This report highlights artificial intelligence (AI) and big data innovations and their applications for Africa’s mining sector. It argues that AI and digitalization offer African states, the United States, and allied partners a strategic opportunity to move beyond legacy constraints and one-sided partnerships in the mining sector. AI and data applications in Africa can help expedite discovery, reduce drilling time and modernize operations, while de-risking investment and supporting policy modeling. AI and data partnerships can potentially advance supply chain transparency, strengthen local capacity across digital and applied sciences, and yield benefits for African communities. As the United States is a leader in global AI, the US government should leverage its expertise in new technologies, mapping, and big data to develop partnerships with African countries that advance US security objectives while providing long-term dividends and economic development for African nations. These efforts should be pursued as a single, integrated “smart mining” agenda rather than as siloed AI and mining investment tracks, so that these investments in African markets support one another. This report also addresses uneven AI adoption and persisting challenges for African economies in future mining, including supply chain vulnerability to external shocks, infrastructure and governance gaps, and labor and skill needs. A successful AI and digitalization transition for African mining will involve concerted efforts to encourage targeted capital in exploration and infrastructure development, US–Africa partnerships with cross-disciplinary staffing and expertise, and the integration of local African expertise across mining ventures.

The Economics of Geological Uncertainty: How Every Geological Assumption Influences Mine Value When discussing Mineral Resource Estimation, many conversations focus on block models, kriging, variograms, or resource classification. However, one critical question is often overlooked: How does geological uncertainty ultimately influence the economic value of a mining project? The answer is simple “every geological assumption propagates throughout the mining value chain” A small uncertainty during sampling, geological interpretation, domaining, density assignment, or grade estimation may appear insignificant at first. Yet as the project progresses through mine design, production scheduling, metallurgical recovery, cash flow forecasting, and economic evaluation, those uncertainties can compound and significantly affect project outcomes. This is why geological uncertainty should never be viewed solely as a technical challenge. It is a business risk that directly influences investment confidence, Net Present Value (NPV), mine planning decisions, and ultimately whether a project creates or destroys value. Rather than attempting to eliminate uncertainty entirely—which is impossible in earth sciences—we should strive to identify, quantify, communicate, and manage uncertainty transparently.Robust geological interpretation, representative sampling, effective QA/QC, appropriate geostatistical methods, continuous model validation, and transparent reporting are fundamental to building confidence in Mineral Resources and supporting sound engineering decisions. As emphasized by internationally recognized reporting standards, Mineral Resources and Ore Reserves should be reported with an appropriate understanding of the level of geological confidence and the Modifying Factors that affect economic extraction. Sound decision-making begins with understanding not only what we know, but also what we do not know. **Key References** • JORC Code (2012). *Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves.* • Rossi, M. E., & Deutsch, C. V. (2014). *Mineral Resource Estimation.* Springer. • Deutsch, C. V. (1997). *Geostatistical Reservoir Modeling.* Oxford University Press. • Dominy, D. F. (2002). *Errors and Uncertainty in Mineral Resource and Ore Reserve Estimation.* • Lane, K. F. (2002). *The Economic Definition of Ore.* • Whittle, J. (2002). *Open Pit Optimization and Strategic Mine Planning.* In your opinion, “which stage in the mining value chain contributes the greatest uncertainty to project economics, and why?” I would be interested to learn from your experience and perspective. #Mining #EconomicGeology #MineralResources #OreReserve #Geostatistics #MinePlanning #MiningEngineering #MiningEconomics #Uncertainty #RiskManagement #JORC #CompetentPerson #GeologicalModeling #DecisionMaking #MiningIndustry

Overview: Metallurgically Based Processing Framework Copper-bearing gold ore processing is a technically complex mineral processing challenge. High levels of soluble copper can increase gold lixiviant consumption, interfere with adsorption performance, and reduce overall gold recovery efficiency. The optimal processing route cannot be determined by copper grade alone. Professional process selection requires comprehensive evaluation of:  Copper mineralogy and solubility characteristics   Gold occurrence and liberation conditions   Oxide or sulfide ore characteristics   Sulfide mineral association   Ore texture and processing behavior   Project CAPEX and OPEX requirements  We provide three test-verifiable solution architectures for copper-bearing gold ores:  Oxide copper-gold ore processing   Sulfide copper-gold ore processing   Ammoniacal copper solution purification systems  These solutions integrate copper removal, gold leaching, flotation pre-treatment, solvent extraction, and adsorption technologies for mining projects in Mongolia, Africa, and Malaysia. Laboratory bottle roll tests and pilot-scale verification are recommended before full-scale industrial implementation.

**Geophysics Beyond Discovery: Driving Reserve Reconciliation and Resource Optimization** In many mining operations, geophysical surveys are often viewed solely as exploration tools. Once a deposit is discovered and modeled, valuable datasets such as IP, TDIP, Resistivity, Magnetics, Gravity, and EM are frequently underutilized. However, the true value of geophysics extends far beyond discovery. When integrated with geology, geostatistics, and mine planning, geophysical data can significantly improve: 🔹 Geological domain definition 🔹 Ore-waste boundary interpretation 🔹 Block model validation 🔹 Grade continuity assessment 🔹 Resource confidence classification 🔹 Mining recovery and dilution control More importantly, geophysics can play a critical role in both monthly and annual reconciliation processes by helping identify the root causes of discrepancies between predicted and actual production performance. A robust reconciliation workflow should not only measure variance—it should continuously improve the geological and resource model. The most successful mining operations create a feedback loop where: *Exploration Data → Geological Understanding → Resource Model → Production → Reconciliation → Model Improvement* This approach transforms geophysics from a discovery tool into a long-term value generator throughout the entire Life of Mine (LoM). *"Geophysics should not stop when exploration ends. It should continue creating value through better decisions, improved confidence, and optimized reserves."* #Mining #Geophysics #Geology #ResourceEstimation #ReserveEstimation #Geostatistics #MinePlanning #Reconciliation #OreControl #MiningEngineering #ResourceModeling #TDIP #InducedPolarization #SafetyFirst

Most people associate geophysics with finding mineral deposits. In reality, geophysical data contribute far beyond discovery. From exploration and resource estimation to mine planning, production, environmental management, and mine closure, geophysics provides critical information that helps reduce uncertainty and improve decision-making. Each method reveals different characteristics of the subsurface: • Magnetic Surveys → Geological structures, lithology, and alteration patterns • Induced Polarization (IP) → Sulfide mineralization and hydrothermal systems • Resistivity Surveys → Groundwater, faults, and weathered zones • Electromagnetic (EM) Surveys → Conductive mineralization and deep targets • Gravity Surveys → Density contrasts and intrusive bodies • Radiometric Surveys → Alteration mapping and REE potential • Seismic Surveys → Rock mass properties and geotechnical conditions • Ground Penetrating Radar (GPR) → Near-surface structures and voids When integrated with geological interpretation, these datasets help mining professionals: ✔ Improve drill targeting accuracy ✔ Reduce exploration risk and unnecessary costs ✔ Enhance resource and reserve confidence ✔ Support geotechnical and hydrogeological assessments ✔ Improve operational planning and mine safety ✔ Strengthen environmental monitoring and mine closure strategies The most successful mining projects are not built on data alone. They are built on the ability to transform data into understanding, and understanding into decisions. Geophysics gives us the measurements. Geology gives us the interpretation. Mining gives us the application. Together, they transform uncertainty into value. **From discovery to closure, geophysical data remain one of the most powerful tools for building safer, smarter, and more profitable mining operations.** #Mining #MineralExploration #Geophysics #Geology #MiningEngineering #ResourceEstimation #ReserveEstimation #Hydrogeology #GeotechnicalEngineering #RareEarthElements #GoldMining #CopperMining #SustainableMining #MinePlanning #ZveniaMining