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Applied Sciences
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Application of Artificial Intelligence in the Mining Industry
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Application of Artificial Intelligence in the Mining Industry

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

Deadline for manuscript submissions: 20 March 2025 | Viewed by 3206

Special Issue Editor

Dr. Yuantian Sun

E-Mail Website
Guest Editor
School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: data analysis using AI and ML; recycling; engineering materials; numerical modeling; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI has been instrumental to the world and has enhanced new techniques and new products. For the mining industry, from exploration, development, and beneficiation to reclamation, AI has been invented and will be applied in every process in mining.

Nowadays, new artificial algorithms and models have been developed and parts of them have started to be utilized in mining industries to improve efficiency and accuracy. Moreover, some models have been used for answering questions and giving advice to managers, miners, or technicians. Due to AI, mining is not an information island, and is instead a knowledge center. In this intelligent center, machines, people, the environment, geology, and engineering have been used in concert to deal with future problems in the mining industry. Many researchers have carried out the relevant work, and this Special Issue will encourage interdisciplinary communication, especially in the mining industry.

This Special Issue will publish high-quality original research papers in the following fields (among others):

  • Application of artificial intelligence;
  • Mining data processing;
  • Numerical modeling;
  • Prediction and regression for mining engineering;
  • New machine learning and deep learning algorithm application;
  • Big data analysis.

Dr. Yuantian Sun
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

  • AI
  • modelling
  • mining
  • prediction
  • machine learning

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Published Papers (3 papers)

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Research

18 pages, 3106 KiB  
Article
Classification of Logging Data Using Machine Learning Algorithms
by Ravil Mukhamediev, Yan Kuchin, Nadiya Yunicheva, Zhuldyz Kalpeyeva, Elena Muhamedijeva, Viktors Gopejenko and Panabek Rystygulov
Appl. Sci. 2024, 14(17), 7779; https://doi.org/10.3390/app14177779 - 3 Sep 2024
Cited by 1 | Viewed by 669
Abstract
A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning [...] Read more.
A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Mining Industry)
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18 pages, 7186 KiB  
Article
Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings
by Yunbo Tao, Qiusong Chen, Chongchun Xiao, Min Zhu and Jianhui Qiu
Appl. Sci. 2024, 14(11), 4771; https://doi.org/10.3390/app14114771 - 31 May 2024
Viewed by 841
Abstract
Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by [...] Read more.
Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by underground mine blasting. Therefore, this study aims to improve the accuracy of predicting ground vibrations induced by underground blasting by comprehensively measuring the peak particle velocity (PPV) in all three directions and independently considering on the impact of vertical distance. Random forest regression (RFR), bagging regression (BR), and gradient boosting regression (GBR) were used to regress the X-axis PPV (X-PPV), Y-axis PPV (Y-PPV), and Z-axis PPV (Z-PPV) based on blasting records measured at an iron mine. In addition, a genetic algorithm, gray wolf optimizer (GWO), and a particle swarm optimization were used to optimize the parameters of the RFR, BR, and GBR. The comparison results show that GWO-GBR is the optimal model for the prediction of the X-PPV (R2 = 0.8072), Y-PPV (R2 = 0.9147), and Z-PPV (R2 = 0.9265), respectively. Thus, the GWO-GBR model proposed in this study is considered a highly reliable model for predicting ground vibrations induced by underground mine blasting to ensure the safety of the mines’ surroundings. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Mining Industry)
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14 pages, 3966 KiB  
Article
Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm
by Lianku Xie, Qinglei Yu, Jiandong Liu, Chunping Wu and Guang Zhang
Appl. Sci. 2024, 14(9), 3839; https://doi.org/10.3390/app14093839 - 30 Apr 2024
Viewed by 1110
Abstract
Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on [...] Read more.
Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the Mining Industry)
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