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Recent Advances and Applications of Artificial Intelligence in the Mining Industry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 1201

Special Issue Editor

Prof. Dr. Rajive Ganguli

E-Mail Website
Guest Editor
Department of Mining Engineering, University of Utah, Salt Lake City, UT 84112-0102, USA
Interests: machine learning; artificial intelligence; mining engineering; systems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to capture the application of various forms of artificial intelligence (AI) to mining industry problems. Some examples of this include AI on structured data such as in orebody modeling and process control, use of generative textual or graphical AI in safety and other applications, and use of augmented/virtual reality. Of special interest are articles that help improve AI, such as those developing new algorithms, or ones that help spread the use of AI, such as development of public domain tools. Given the fast pace of this topic, literature review papers are not of particular interest, nor are articles presenting as advertisements for commercial products.

Prof. Dr. Rajive Ganguli
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • mining industry
  • mineral exploration
  • healthy and safety
  • artificial intelligence
  • mining engineering
  • machine learning

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Published Papers (2 papers)

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Research

19 pages, 16342 KiB  
Article
Revolutionizing Open-Pit Mining Fleet Management: Integrating Computer Vision and Multi-Objective Optimization for Real-Time Truck Dispatching
by Kürşat Hasözdemir, Mert Meral and Muhammet Mustafa Kahraman
Appl. Sci. 2025, 15(9), 4603; https://doi.org/10.3390/app15094603 - 22 Apr 2025
Viewed by 297
Abstract
The implementation of fleet management software in mining operations poses challenges, including high initial costs and the need for skilled personnel. Additionally, integrating new software with existing systems can be complex, requiring significant time and resources. This study aims to mitigate these challenges [...] Read more.
The implementation of fleet management software in mining operations poses challenges, including high initial costs and the need for skilled personnel. Additionally, integrating new software with existing systems can be complex, requiring significant time and resources. This study aims to mitigate these challenges by leveraging advanced technologies to reduce initial costs and minimize reliance on highly trained employees. Through the integration of computer vision and multi-objective optimization, it seeks to enhance operational efficiency and optimize fleet management in open-pit mining. The objective is to optimize truck-to-excavator assignments, thereby reducing excavator idle time and deviations from production targets. A YOLO v8 model, trained on six hours of mine video footage, identifies vehicles at excavators and dump sites for real-time monitoring. Extracted data—including truck assignments and excavator ready times—is incorporated into a multi-objective binary integer programming model that aims to minimize excavator waiting times and discrepancies in target truck assignments. The epsilon-constraint method generates a Pareto frontier, illustrating trade-offs between these objectives. Integrating real-time image analysis with optimization significantly improves operational efficiency, enabling adaptive truck-excavator allocation. This study highlights the potential of advanced computer vision and optimization techniques to enhance fleet management in mining, leading to more cost-effective and data-driven decision-making. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Artificial Intelligence in the Mining Industry)
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15 pages, 1008 KiB  
Article
BoxRF: A New Machine Learning Algorithm for Grade Estimation
by Ishmael Anafo, Rajive Ganguli and Narmandakh Sarantsatsral
Appl. Sci. 2025, 15(8), 4416; https://doi.org/10.3390/app15084416 - 17 Apr 2025
Viewed by 345
Abstract
A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inverse distance methods, with the robustness of random forest [...] Read more.
A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inverse distance methods, with the robustness of random forest (RF) methods that come from forming numerous random groups of data. The method was applied to a porphyry copper deposit, and results were compared to various ML methods, including XGBoost (XGB), k-nearest neighbors (KNN), neural nets (NN), and RF. Scikit-learn RF (SRF) performed the best (R2 = 0.696) among the ML methods but underperformed BoxRF (R2 = 0.751). The results were confirmed through a five-fold cross-validation exercise where BoxRF once again outperformed SRF. The box dimensions that performed the best were similar in length to the ranges indicated by variogram modeling, thus demonstrating a link between machine learning and traditional methods. Numerous combinations of hyperparameters performed similarly well, implying the method is robust. The inverse distance method was found to better represent the grade–space relationship in BoxRF than median values. The superiority of BoxRF over SRF in this dataset is encouraging, as it opens the possibility of improving machine learning by incorporating domain knowledge (principles of geology, in this case). Full article
(This article belongs to the Special Issue Recent Advances and Applications of Artificial Intelligence in the Mining Industry)
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