Geometallurgy: A tool for optimizing the value of deposits and generating more efficient mining operations

Main Article Content

Nelson Ramos
https://orcid.org/0000-0001-9188-6422
Marilú Calderón-Celis
https://orcid.org/0000-0002-1374-9307

Abstract

Mining operations are conditioned to geometallurgical attributes that are inherently variable due to the natural heterogeneity of the deposits. In view of this, geometallurgy as an emerging discipline provides a fundamental support to evaluate the uncertainty in primary and response variables, however, for its use it is necessary to know the theoretical bases that allow its applicability. Consequently, the objective of this research consisted in elaborating a literature review and to present the fundamentals that support geometallurgy together with case studies where this innovative discipline has been successfully used. For this purpose, the methodology was to use a search strategy in the Scopus database considering key words, using Boolean operators, and among the articles found, the most relevant ones were chosen and a bibliometric analysis was carried out using the VOSviewer software; in addition, complementary information was collected in the indicated database taking into account the focus of this research and conference papers, books and NI 43 - 101 report were also included, all of them in english language. The results show that the effect and proper understanding of geometallurgical variables in the sampling, domain definition and subsequent modeling is fundamental for an adequate mapping of the variability in the ore behavior during processing. Finally, it is concluded that the use of mineralogy to determine species that interfere in the treatment, geostatistics and particularly cokriging for the prediction of copper mass in the feed and concentrate from which the recovery can be determined and machine learning as a tool to elaborate the geometallurgical modeling, are techniques that allow optimizing the value of the deposit and manage the mining operations in a more efficient way.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Ramos, N., & Calderón-Celis, M. (2025). Geometallurgy: A tool for optimizing the value of deposits and generating more efficient mining operations. FIGEMPA: Investigación Y Desarrollo, 20(2), e8384. https://doi.org/10.29166/revfig.v20i2.8384
Section
Artículos
Author Biographies

Nelson Ramos, Universidad Nacional Mayor de San Marcos. Lima, Perú.

Unidad de Posgrado. Facultad de Ingeniería Geológica, Minera, Metalúrgica y Geográfica. Grupo de Investigación de Tecnología Limpia - VRIP - UNMSM. Ciudadela Universitaria, Av. Venezuela. 15081. Lima, Perú.

Marilú Calderón-Celis, Universidad Nacional Mayor de San Marcos. Lima, Perú

Unidad de Posgrado. Facultad de Ingeniería Geológica, Minera, Metalúrgica y Geográfica. Grupo de Investigación de Tecnología Limpia - VRIP - UNMSM. Ciudadela Universitaria, Av. Venezuela. 15081. Lima, Perú.

References

Aasly, K. (2024) “Process mineralogy of unconventional mineral deposits examples of applications and challenges”, Minerals Engineering, 209. Doi: 10.1016/j.mineng.2024.108649

Abedini, A., Calagari, A.A. y Khosravi, M. (2025) “The Trace Element Geochemistry of the Vali–Janlou Kaolin Deposit, Urmia–Dokhtar Magmatic Belt, Central-Northern Iran”, Geosciences, 15(2), pp. 58-74. Doi: 10.3390/geosciences15020058

Abildin, Y. et al. (2023) “Geometallurgical Responses on Lithological Domains Modelled by a Hybrid Domaining Framework”, Minerals, 13(7). Doi: 10.3390/min13070918

Addo, E. et al. (2019) “Prediction of copper recovery from geometallurgical data using D-vine copulas”, Journal of the Southern African Institute of Mining and Metallurgy, 119(4), pp. 339-346. Doi: 10.17159/2411-9717/319/2019

Adeli, A. et al. (2021) “Using cokriging to predict metal recovery accounting for non-additivity and preferential sampling designs”, Minerals Engineering, 170. Doi: 10.1016/j.mineng.2021.106923

Altinkaya, P. et al. (2020) “Leaching and recovery of gold from ore in cyanide-free glycine media”, Minerals Engineering, 158. Doi: 10.1016/j.mineng.2020.106610

Alves Campos, P.H. et al. (2024) “Short-Term Schedule Optimization with Nonlinear Blending Models for Improved Metallurgical Recovery in Mining”, Mining, Metallurgy and Exploration, 41(4), pp. 1629-1643. Doi: 10.1007/s42461-024-00986-4

Aras, A., Özşen, H. y Dursun, A.E. (2020) “Using Artificial Neural Networks for the Prediction of Bond Work Index from Rock Mechanics Properties”, Mineral Processing and Extractive Metallurgy Review, 41(3), pp. 145-152. Doi: 10.1080/08827508.2019.1575216

Auguścik-Górajek, J. et al. (2021) “Problems of estimating the resources of accompanying elements: A case study from the cu-ag rudna deposit (Legnica-Głogów Copper district, Poland)”, Minerals, 11(12). Doi: 10.3390/min11121431

Avalos, S., Kracht, W. y Ortiz, J.M. (2020) “An LSTM approach for SAG Mill Operational Relative-Hardness Prediction”, Minerals, 10(9). Doi: 10.3390/min10090734

Ayedzi, L.D. et al. (2024) “Characterization of a Nickel Sulfide Concentrate and Its Implications on Pentlandite Beneficiation”, Minerals, 14(4). Doi: 10.3390/min14040414

Azarafza, M., Hajialilue Bonab, M. and Derakhshani, R. (2022) “A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone”, Materials, 15(19). Doi: 10.3390/ma15196899

Badakhshan, N. et al. (2024) “Optimization of transition from open-pit to underground mining considering environmental costs”, Resources Policy, 95. Doi: 10.1016/j.resourpol.2024.105178

Baumgartner, R. et al. (2011) “Building a Geometallurgical Model for Early-Stage Project Development – A Case Study from the Canahuire Epithermal Au-Cu-Ag Deposit, Southern Peru”, First AusIMM International Geometallurgy Conference (GeoMet) 2011. Brisbane, pp. 53-60. Disponible en: https://www.ausimm.com/publications/conference-proceedings/first-ausimm-international-geometallurgy-conference-geomet-2011/building-a-geometallurgical-model-for-early-stage-project-development---a-case-study-from-the-canahuire-epithermal-au-cu-ag-deposit-southern-peru/

Beaumont, C. and Musingwini, C. (2019) “Application of geometallurgical modelling to mine planning in a copper-gold mining operation for improving ore quality and mineral processing efficiency”, Journal of the Southern African Institute of Mining and Metallurgy, 119(3), pp. 243-252. Doi: 10.17159/2411-9717/2019/v119n3a3

Behnamfard, A., Namaei Roudi, D. and Veglio, F. (2020) “The performance improvement of a full-scale autogenous mill by setting the feed ore properties”, Journal of Cleaner Production, 271. Doi: 10.1016/j.jclepro.2020.122554

Beland Lindahl, K. et al. (2023) “Factors affecting local attitudes to mineral exploration: What”s within the company”s control?”, Resources Policy, 84. Doi: 10.1016/j.resourpol.2023.103715

Benavente, O. et al. (2019) “Copper extraction from black copper ores through modification of the solution potential in the irrigation solution”, Metals, 9(12). Doi: 10.3390/met9121339

Bhuiyan, M., Esmaeili, K. y Ordóñez Calderón, J.C. (2022) “Evaluation of rock characterization tests as geometallurgical predictors of bond work index at the Tasiast Mine, Mauritania”, Minerals Engineering, 175. Doi: 10.1016/j.mineng.2021.107293

Binnemans, K. and Jones, P.T. (2023) “The Twelve Principles of Circular Hydrometallurgy”, Journal of Sustainable Metallurgy, 9, pp. 1-25. Doi: 10.1007/s40831-022-00636-3

Both, C. and Dimitrakopoulos, R. (2021) “Applied machine learning for geometallurgical throughput prediction—a case study using production data at the tropicana gold mining complex”, Minerals, 11(11). Doi: 10.3390/min11111257

Burns, N. et al. (2019) Technical Report - Salobo III Expansion. Pará State, Brazil: Wheaton Precious Metals. Disponible en: https://minedocs.com/21/Salobo-TR-12312019.pdf

Butcher, A.R. et al. (2023) “Characterisation of Ore Properties for Geometallurgy”, Elements, 19(6), pp. 352-358. Doi: 10.2138/gselements.19.6.352

Carrillo Rosúa, J. et al. (2021) “Application of the mineralogy and mineral chemistry of carbonates as a genetic tool in the hydrothermal environment”, Minerals, 11(8). Doi: 10.3390/min11080822

Cilek, E.C. y Uysal, K. (2018) “Froth stabilisation using nanoparticles in mineral flotation”, Physicochemical Problems of Mineral Processing, 54(3), pp. 878-889. Disponible en: https://www.journalssystem.com/ppmp/pdf-85719-26410?filename=Froth+stabilisation+using.pdf

Coward, S. et al. (2009) “The Primary-Response Framework for Geometallurgical Variables”, Seventh International Mining Geology Conference 2009. Perth, pp. 109-113. Disponible en: https://www.ausimm.com/publications/conference-proceedings/seventh-international-mining-geology-conference-2009/the-primary-response-framework-for-geometallurgical-variables/

Crespo, J. et al. (2024) “Characteristics and evolution of quartz-calcite-sulfide veins in the Nazca-Ocoña belt, Peru”, Ore Geology Reviews, 165. Doi: 10.1016/j.oregeorev.2024.105895

De Castro, B. et al. (2022) “Automated mineralogical characterization using optical microscopy: Review and recommendations”, Minerals Engineering, 189. Doi: 10.1016/j.mineng.2022.107896

Dehaine, Q. et al. (2021) “Geometallurgy of cobalt ores: A review”, Minerals Engineering, 160. Doi: 10.1016/j.mineng.2020.106656

Dominy, S. C., O´Connor, L., Parbhakar-Fox, A., Glass, H. J., y Purevgerel, S. (2018a) “Geometallurgy - A route to more resilient mine operations”, Minerals, 8(12). Doi: 10.3390/min8120560

Dominy, S. C., O´Connor, L., Glass, H. J., Purevgerel, S., y Xie, Y. (2018b) “Towards representative metallurgical sampling and gold recovery testwork programmes”, Minerals, 8(5). Doi: 10.3390/min8050193

Dominy, S.C. and Glass, H.J. (2025) “Geometallurgical Sampling and Testwork for Gold Mineralisation: General Considerations and a Case Study”, Minerals, 15(4). Doi: 10.3390/min15040370

Egaña, Á.F. et al. (2020) “A robust stochastic approach to mineral hyperspectral analysis for geometallurgy”, Minerals, 10(12). Doi: 10.3390/min10121139

Ellefmo, S.L. et al. (2019) “Geometallurgical concepts used in industrial mineral production”, Economic Geology, 114(8), pp. 1543-1554. Doi: 10.5382/econgeo.4685

Estay, H. et al. (2023) “On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy”, Minerals, 13(6). Doi: 10.3390/min13060788

Faramarzi, F. et al. (2020) “The extended drop weight testing approach - What it reveals”, Minerals Engineering, 157. Doi: 10.1016/j.mineng.2020.106550

Frenzel, M. et al. (2023) “Geometallurgy: Present and Future”, Elements, 19(6), pp. 345-351. Doi: 10.2138/gselements.19.6.345

Garrido, M. et al. (2019) “Change of support using non-additive variables with Gibbs Sampler: Application to metallurgical recovery of sulphide ores”, Computers and Geosciences, 122, pp. 68-76. Doi: 10.1016/j.cageo.2018.10.002

Garrido, M. et al. (2020) “Simulation of Synthetic Exploration and Geometallurgical Database of Porphyry Copper Deposits for Educational Purposes”, Natural Resources Research, 29(6), pp. 3527-3545. Doi: 10.1007/s11053-020-09692-6

Gaudin, A. (1932) Principles of Mineral Dressing. New York: McGarw-Hill Book Company. Disponible en: https://archive.org/details/principlesofmine0000amga/page/n7/mode/2up

Gholami, A. et al. (2022) “A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine”, Physicochemical Problems of Mineral Processing, 58(3). Doi: 10.37190/ppmp/147841

Gholami, A., Tokac, B. and Zhang, Q. (2024) “Knowledge synthesis on the mine life cycle and the mining value chain to address climate change”, Resources Policy, 95. Doi: 10.1016/j.resourpol.2024.105183

Gontijo, F. et al. (2023) “Recursive Feature Elimination and Neural Networks Applied to the Forecast of Mass and Metallurgical Recoveries in A Brazilian Phosphate Mine”, Minerals, 13(6). Doi: 10.3390/min13060748

Gontijo, F. et al. (2025) “A workflow to create geometallurgical clusters without looking directly at geometallurgical variables”, Minerals Engineering, 222. Doi: 10.1016/j.mineng.2024.109171

Guimarães Bergerman, M. et al. (2023) “Development of a simplified test for the determination of the Bond Ball Mill Work Index using a modified Hardgrove test”, Minerals Engineering, 203. Doi: 10.1016/j.mineng.2023.108359

Guntoro, P.I., Ghorbani, Y. and Rosenkranz, J. (2021) “3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing”, BHM Berg- und Hüttenmännische Monatshefte, 166(8), pp. 384-389. Doi: 10.1007/s00501-021-01135-w

Hamraoui, L. et al. (2024) “Towards a Circular Economy in the Mining Industry: Possible Solutions for Water Recovery through Advanced Mineral Tailings Dewatering”, Minerals, 14(3). Doi: 10.3390/min14030319

Hernández, H. et al. (2024) “Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model”, Minerals, 14(7). Doi: 10.3390/min14070691

Hoffimann, J. et al. (2022) “Modeling Geospatial Uncertainty of Geometallurgical Variables with Bayesian Models and Hilbert–Kriging”, Mathematical Geosciences, 54(7), pp. 1227-1253. Doi: 10.1007/s11004-022-10013-1

Hornn, V. et al. (2020) “Agglomeration-flotation of finely ground chalcopyrite using emulsified oil stabilized by emulsifiers: Implications for porphyry copper ore flotation”, Metals, 10(7). Doi: 10.3390/met10070912

Iglesias-Martínez, M. et al. (2024) “Exploration and mining of lateritic gold (Part II): Resource estimation, geometallurgy and environmental considerations”, Ore Geology Reviews, 172. Doi: 10.1016/j.oregeorev.2024.106207

Jafarpour, A. and Khatami, S. (2021) “Analysis of Environmental Costs´ Effect in Green Mining Strategy Using a System Dynamics Approach: A Case Study”, Mathematical Problems in Engineering, 2021. Doi: 10.1155/2021/4893776

Jia, Y. et al. (2024) “Multi-Scale and Trans-Disciplinary Research and Technology Developments of Heap Bioleaching”, Minerals, 14(8). Doi: 10.3390/min14080808

Kabemba, A.M., Mutombo, K. and Waters, K.E. (2025) “Towards a Dynamic Optimisation of Comminution Circuit Under Geological Uncertainties”, Processes, 13(2). Doi: 10.3390/pr13020443

Karimov, K. et al. (2024) “Purification of Copper Concentrate from Arsenic under Autoclave Conditions”, Metals, 14(2). Doi: 10.3390/met14020150

Käyhkö, T. et al. (2022) “Validation of predictive flotation models in blended ores for concentrator process design”, Minerals Engineering, 185. Doi: 10.1016/j.mineng.2022.107685

Khorram, F., Asghari, O. and Memarian, H. (2020) “Geometallurgical resource estimation using a modified geostatistical approach; a case study of Sungun porphyry copper deposit, Iran”, Arabian Journal of Geosciences, 13(12). Doi: 10.1007/s12517-020-05327-5

Kim, S. et al. (2020) “Laboratory testing of scheelite flotation from raw ore in sangdong mine for process development”, Minerals, 10(11). Doi: 10.3390/min10110971

Koch, P.H. and Rosenkranz, J. (2020) “Sequential decision-making in mining and processing based on geometallurgical inputs”, Minerals Engineering, 149. Doi: 10.1016/j.mineng.2020.106262

Konieczna-Fuławka, M. et al. (2023) “Challenges Related to the Transformation of Post-Mining Underground Workings into Underground Laboratories”, Sustainability, 15(13). Doi: 10.3390/su151310274

Lai, J. et al. (2024) “A study on the correlation between fractal dimension and particle breakage for tungsten ores under impact crushing”, Minerals Engineering, 218. Doi: 10.1016/j.mineng.2024.108980

Larrabure, G. et al. (2024) “A review on strategies to assess the spatiotemporal heterogeneity of column leaching experiments for heap leaching upscaling”, Minerals Engineering, 216. Doi: 10.1016/j.mineng.2024.108892

Li, C. and Zhang, H. (2022) “Surface nanobubbles and their roles in flotation of fine particles - A review”, Journal of Industrial and Engineering Chemistry, 106, pp. 37-51. Doi: 10.1016/j.jiec.2021.11.009

Li, J. et al. (2023) “Investigation into Mining Economic Evaluation Approaches Based on the Rosenblueth Point Estimate Method”, Applied Sciences, 13(15). Doi: 10.3390/app13159011

Li, S., Wang, Y. and Xie, X. (2021) “Prediction of uniaxial compression strength of limestone based on the point load strength and svm model”, Minerals, 11(12). Doi: 10.3390/min11121387

Lindi, O.T. et al. (2024) “Uncertainty Quantification in Mineral Resource Estimation”, Natural Resources Research, 33(6), pp. 2503-2526. Doi: 10.1007/s11053-024-10394-6

Lishchuk, V., Lund, C. and Ghorbani, Y. (2019) “Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy”, Minerals Engineering, 134, pp. 156-165. Doi: 10.1016/j.mineng.2019.01.032

Lishchuk, V. and Pettersson, M. (2021) “The mechanisms of decision-making when applying geometallurgical approach to the mining industry”, Mineral Economics, 34(1), pp. 71-80. Doi: 10.1007/s13563-020-00220-9

Liu, Y. and Wen, S. (2023) “Characteristics of Gold Minerals in Gold Concentrate with a High Copper Content and Effective Gold Recovery via Flotation and Ammonia Pretreatment-Cyanidation Leaching”, Minerals, 13(8). Doi: 10.3390/min13081088

Ma, Y. et al. (2022) “Flotation separation mechanism for secondary copper sulfide minerals and pyrite using novel collector ethyl isobutyl xanthogenic acetate”, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 634. Doi: 10.1016/j.colsurfa.2021.128010

Madenova, Y. and Madani, N. (2021) “Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables”, Natural Resources Research, 30(2), pp. 1199-1228. Doi: 10.1007/s11053-020-09802-4

Maleki, M. et al. (2020) “Stochastic open-pit mine production scheduling: A case study of an iron deposit”, Minerals, 10(7). Doi: 10.3390/min10070585

Maniteja, M. et al. (2025) “Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging”, Minerals, 15(2). Doi: 10.3390/min15020131

McQuiston, F.W. and Bechaud, L.J. (1968) Metallurgical sampling and testing. New York: American Institute of Mining, Metallurgical and Petroleum Engineers, pp. 103–121.

Mlambo, C. (2022) “Non-Renewable Resources and Sustainable Resource Extraction: An Empirical Test of the Hotelling Rule´s Significance to Gold Extraction in South Africa”, Sustainability, 14(17). Doi: 10.3390/su141710619

Moosavi-Khoonsari, E. and Tripathi, N. (2024) “Gold Recovery from Smelting Copper Sulfide Concentrate”, Processes, 12(12). Doi: 10.3390/pr12122795

Moraga, C., Kracht, W. and Ortiz, J.M. (2022) “Process simulation to determine blending and residence time distribution in mineral processing plants”, Minerals Engineering, 187. Doi: 10.1016/j.mineng.2022.107807

Morales, N. et al. (2019) “Incorporation of geometallurgical attributes and geological uncertainty into long-term open-pit mine planning”, Minerals, 9(2). Doi: 10.3390/min9020108

Mu, Y. and Salas, J.C. (2023) “Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit”, Processes, 11(6). Doi: 10.3390/pr11061775

Müller, A., Kirwin, D. and Seltmann, R. (2023) “Textural characterization of unidirectional solidification textures related to Cu–Au deposits and their implication for metallogenesis and exploration”, Mineralium Deposita, 58(7), pp. 1211-1235. Doi: 10.1007/s00126-023-01175-x

Navarra, A., Grammatikopoulos, T. and Waters, K. (2018) “Incorporation of geometallurgical modelling into long-term production planning”, Minerals Engineering, 120, pp. 118-126. Doi: 10.1016/j.mineng.2018.02.010

Nikolić, V. et al. (2024) “Methods for Estimating the Bond Work Index for Ball Mills”, Minerals, 14(12). Doi: 10.3390/min14121264

Nikolić, V. and Trumić, M. (2021) “A new approach to the calculation of bond work index for finer samples”, Minerals Engineering, 165. Doi: 10.1016/j.mineng.2021.106858

Nwaila, G.T. et al. (2020) “Geometallurgical Approach for Implications of Ore Blending on Cyanide Leaching and Adsorption Behavior of Witwatersrand Gold Ores, South Africa”, Natural Resources Research, 29(2), pp. 1007-1030. Doi: 10.1007/s11053-019-09522-4

Nwaila, G.T. et al. (2024) “Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models”, Natural Resources Research, 33(1), pp. 129-161. Doi: 10.1007/s11053-023-10280-7

Órdenes, J. et al. (2021) “Incorporation of geometallurgical input into gold mining system simulation to control cyanide consumption”, Minerals, 11(9). Doi: 10.3390/min11091023

Pan, Z. et al. (2024) “Study on Process Mineralogy of the Combined Copper Oxide Ore in Tibet and Acid Leaching Behavior with Calcium Fluoride”, Minerals, 14(4). Doi: 10.3390/min14040352

Panayotov, V. and Panayotova, M. (2023) “Technology for increasing the precious metals content in copper concentrate obtained by flotation”, Physicochemical Problems of Mineral Processing, 59(5). Doi: 10.37190/ppmp/167424

Perea, C.G. et al. (2024) “Study of a Copper Oxide Leaching in Alkaline Monosodium Glutamate Solution”, Minerals, 14(7). Doi: 10.3390/min14070714

Pereira, G. et al. (2022) “A multi-methodological approach for mineral exploration and predictive metallurgy: the case of the Pilar gold deposit at the Quadrilátero Ferrífero, Brazil”, Ore Geology Reviews, 149. Doi: 10.1016/j.oregeorev.2022.105113

Prior, Á. et al. (2021) “Resource Model Updating For Compositional Geometallurgical Variables”, Mathematical Geosciences, 53(5), pp. 945-968. Doi: 10.1007/s11004-020-09874-1

Quezada, V. et al. (2024) “Effect of Pretreatment on a Copper Concentrate with High Arsenic Content”, Minerals, 14(4). Doi: 10.3390/min14040419

Rajabinasab, B. and Asghari, O. (2019) “Geometallurgical Domaining by Cluster Analysis: Iron Ore Deposit Case Study”, Natural Resources Research, 28(3), pp. 665-684. Doi: 10.1007/s11053-018-9411-6

Ranjbar, A., Mousavi, A. and Asghari, O. (2021) “Using Rock Geomechanical Characteristics to Estimate Bond Work Index for Mining Production Blocks”, Mining, Metallurgy and Exploration, 38(6), pp. 2569-2583. Doi: 10.1007/s42461-021-00498-5

Ren, S.T. et al. (2022) “Extended Ultimate-Pit-Limit Methodology for Optimizing Surface-to-Underground Mining Transition in Metal Mines”, Advances in Civil Engineering, 2022. Doi: 10.1155/2022/2753991

Rincon, J., Gaydardzhiev, S. and Stamenov, L. (2019a) “Coupling comminution indices and mineralogical features as an approach to a geometallurgical characterization of a copper ore”, Minerals Engineering, 130, pp. 57-66. Doi: 10.1016/j.mineng.2018.10.007

Rincon, J., Gaydardzhiev, S. and Stamenov, L. (2019b) “Investigation on the flotation recovery of copper sulphosalts through an integrated mineralogical approach”, Minerals Engineering, 130, pp. 36-47. Doi: 10.1016/j.mineng.2018.10.006

Rodovalho, E. et al. (2025) “An Advanced Approach for Geometallurgical Modeling Applied to Bauxite Mines”, Mining, 5(1). Doi: 10.3390/mining5010011

Schulz, B., Sandmann, D. and Gilbricht, S. (2020) “SEM-based automated mineralogy and its application in geo-and material sciences”, Minerals. Multidisciplinary Digital Publishing Institute, 10(11). Doi: 10.3390/min10111004

Siddiqui, M.U. et al. (2024) “An Efficient Sample Selection Methodology for a Geometallurgy Study Utilizing Statistical Analysis Techniques”, Mining, Metallurgy and Exploration, 41(4), pp. 2193-2201. Doi: 10.1007/s42461-024-01011-4

Tijsseling, L.T. et al. (2020) “Mineralogical prediction of flotation performance for a sediment-hosted copper–cobalt sulphide ore”, Minerals, 10(5). Doi: 10.3390/min10050474

Tiu, G. et al. (2021) “Tracking silver in the Lappberget Zn-Pb-Ag-(Cu-Au) deposit, Garpenberg mine, Sweden: Towards a geometallurgical approach”, Minerals Engineering, 167. Doi: 10.1016/j.mineng.2021.106889

Tiu, G. et al. (2023) “Quantifying the variability of a complex ore using geometallurgical domains”, Minerals Engineering, 203. Doi: 10.1016/j.mineng.2023.108323

Vinnett, L. et al. (2020) “Analysis of flotation rate distributions to assess erratic performances from size-by-size kinetic tests”, Minerals Engineering, 149. Doi: 10.1016/j.mineng.2020.106229

Warlo, M. et al. (2019) “Automated quantitative mineralogy optimized for simultaneous detection of (precious/critical) rare metals and base metals in a production-focused environment”, Minerals, 9(7). Doi: 10.3390/min9070440

Wellmer, F.W. (2022) “Geology and Mining: A Symbiotic Cooperation?!”, Mining, 2(2), pp. 402-424. Doi: 10.3390/mining2020021

Wieczorek, A.N. et al. (2024) “Effect of the Mineralogical Composition of Sandstones on the Wear of Mining Machinery Components”, Coatings, 14(7). Doi: 10.3390/coatings14070859

Wu, J. et al. (2022) “Research on Multi-Objective Ore Blending Optimization Based on Non-Equilibrium Grade Polymetallic Mine of Shizhuyuan”, Minerals, 12(11). Doi: 10.3390/min12111358

Yenial-Arslan, U. et al. (2023) “Pathway to Prediction of Pyrite Floatability from Copper Ore Geological Domain Data”, Minerals, 13(6). Doi: 10.3390/min13060801

Zhou, S. et al. (2023) “Evaluation of Portable X-ray Fluorescence Analysis and Its Applicability As a Tool in Geochemical Exploration”, Minerals, 13(2). Doi: 10.3390/min13020166

Zhou, X. et al. (2022) “Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument”, Applied Sciences, 12(19). Doi: 10.3390/app12199763