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Minerals
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Application of Machine Learning in Mining, Mineral Processing and Extractive...
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Application of Machine Learning in Mining, Mineral Processing and Extractive Metallurgy

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: 29 August 2025 | Viewed by 1143

Special Issue Editors

Prof. Dr. Javier Ruiz del Solar

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
Interests: mining automation; deep learning; reinforcement learning; applications of machine learning and computer vision in mining
Dr. Humberto Estay

E-Mail Website
Guest Editor
Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago 8380418, Chile
Interests: process engineering; hydrometallurgy; extractive metallurgy; membrane technologies; mine waste valorization
Special Issues, Collections and Topics in MDPI journals
Dr. Pia Lois-Morales

E-Mail Website
Guest Editor
Department of Mining Engineering, University of Chile, Santiago 8370451, Chile
Interests: geo-mining-metallurgy; process mineralogy; advanced characterizacion; comminution modeling

Special Issue Information

Dear Colleagues,

Machine learning (ML) is one of the most exciting areas in artificial intelligence. Following several decades of the development of different learning techniques, ML is currently being applied in practically all industries. In the fields of mining, mineral processing, and extractive metallurgy, the application of machine learning, and in particular deep learning, has important benefits. Among them, the following stand out: the predictive modeling of processes, which facilitates the prediction of their behavior, the variability of their outputs, and improved control of them; the real-time analysis of material flow to optimize process performance through real-time analysis of operational variables; the predictive maintenance of equipment and components; and the optimization of the use of energy and water. The following Special Issue aims to present innovative applications of ML in mining, mineral processing, and extractive metallurgy, in addition to the quantitative benefits of the applications of ML in real mining plants.

Prof. Dr. Javier Ruiz del Solar
Dr. Humberto Estay
Dr. Pia Lois-Morales
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning applied to mining, mineral processing, and extractive metallurgy
  • predictive modeling of mining and metallurgical processes
  • machine learning-based control of mining and metallurgical processes
  • machine learning-based water and energy optimization in mining
  • machine learning-based optimization of mining and metallurgical processes
  • predictive maintenance of equipment and components
  • optimization strategies and modeling of metallurgical process performance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 5048 KiB  
Article
Estimation of Copper Grade, Acid Consumption, and Moisture Content in Heap Leaching Using Extended and Unscented Kalman Filters
by Lisbel Bárzaga-Martell, Simón Diaz-Quezada, Humberto Estay and Javier Ruiz-del-Solar
Minerals 2025, 15(5), 521; https://doi.org/10.3390/min15050521 - 14 May 2025
Viewed by 190
Abstract
The leaching process is essential in the mining industry, because it efficiently extracts valuable minerals, such as copper. However, monitoring and controlling the leaching process presents significant challenges due to material variability, uneven distribution of the leaching solution, and the effects of environmental [...] Read more.
The leaching process is essential in the mining industry, because it efficiently extracts valuable minerals, such as copper. However, monitoring and controlling the leaching process presents significant challenges due to material variability, uneven distribution of the leaching solution, and the effects of environmental factors like temperature and moisture content. One of the main technological challenges is measuring variables within the leaching heap. Implementing state observers or estimators (i.e., virtual sensors) offers a promising solution, allowing for a cost-effective estimation of non-measurable process variables. To validate this approach, this paper proposes and analyzes the use of two estimation methods, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), to estimate the moisture content, copper in the ore, and acid consumption based on measurements of acid and copper concentrations in the heap leaching process. The results obtained from simulations demonstrate accurate estimations from both state observers. The variable best estimated with EKF was the moisture content, achieving a 0.041% Integral Absolute Error (IAE) and a 0.069% Integral Square Error (ISE) in one of the analyzed scenarios. Utilizing these state estimators improves the understanding of the internal dynamics of heap leaching, often limited by the lack of field-level instrumentation, such as sensors and transmitters. This approach can enhance the operational efficiency of heap leaching plants by enabling the real-time estimation of unmeasurable variables, ultimately improving metal recovery and reducing acid consumption. Full article
(This article belongs to the Special Issue Application of Machine Learning in Mining, Mineral Processing and Extractive Metallurgy)
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19 pages, 11115 KiB  
Article
Machine Learning Algorithm-Based Prediction Model and Software Implementation for Strength Efficiency of Cemented Tailings Fills
by Hui Cao, Aiai Wang, Erol Yilmaz and Shuai Cao
Minerals 2025, 15(4), 405; https://doi.org/10.3390/min15040405 - 11 Apr 2025
Viewed by 391
Abstract
A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive [...] Read more.
A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) was applied to existing AI and soft computing techniques, using AdaBoost, random forest (RF), SVM, and ANN. Data were arbitrarily separated into training (70%) and test (30%) sets. Results confirm that AdaBoost and RF have the best prediction accuracy, with a correlation coefficient (R2) of 0.957 between these sets for AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 3.9 64-bit), and PyQt5 to achieve the machine learning model computation and software function interface development, the application of this software can quickly predict the strength property of CTF specimens, which saves time and costs efficiently for backfill researchers and developing new eco-efficient components. Full article
(This article belongs to the Special Issue Application of Machine Learning in Mining, Mineral Processing and Extractive Metallurgy)
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Graphical abstract

19 pages, 854 KiB  
Article
Correlation and Knowledge-Based Joint Feature Selection for Copper Flotation Backbone Process Design
by Haipei Dong, Fuli Wang, Dakuo He and Yan Liu
Minerals 2025, 15(4), 353; https://doi.org/10.3390/min15040353 - 27 Mar 2025
Viewed by 187
Abstract
The intelligentization of flotation design plays a crucial role in enhancing industrial competitiveness and resource efficiency. Our previous work established a mapping relationship between the flotation backbone process graph and label vectors, enabling an intelligent design of the copper flotation backbone process through [...] Read more.
The intelligentization of flotation design plays a crucial role in enhancing industrial competitiveness and resource efficiency. Our previous work established a mapping relationship between the flotation backbone process graph and label vectors, enabling an intelligent design of the copper flotation backbone process through multilabel classification. Due to the insufficient quantity of training samples in historical databases, traditional feature selection methods perform poorly, owing to insufficient learning. To address the label-specific feature selection problem for this design, this study proposes correlation and knowledge-based joint feature selection (CK-JFS). In this proposed method, label correlations ensure that features specific to strongly related labels are prioritized, while domain knowledge further refines the selection process by applying specialized knowledge to copper flotation. This mode of data and knowledge integration significantly reduces the reliance of label-specific feature selection on the number of training samples. The results demonstrate that CK-JFS achieves significantly higher accuracy and computational efficiency compared to traditional multilabel feature selection algorithms in the context of copper flotation backbone process design. Full article
(This article belongs to the Special Issue Application of Machine Learning in Mining, Mineral Processing and Extractive Metallurgy)
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