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Processes
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Data-Driven Analysis and Simulation of Coal Mining
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Data-Driven Analysis and Simulation of Coal Mining

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 515

Special Issue Editors

Dr. Huichao Yin

E-Mail Website
Guest Editor
Department of Plant & Environmental Science, New Mexico State University, Las Cruces, NM 88003, USA
Interests: coal mining; hydrogeology; deep/machine learning algorithms; hydroinformatics; geohazard assessment
Prof. Dr. Huiqing Lian

E-Mail Website
Guest Editor
College of Safety Engineering, North China Institute of Science and Technology, Beijing 101601, China
Interests: intelligent mining; mine water hazard prediction; emergency management

Special Issue Information

Dear Colleagues,

The coal mining industry is undergoing a significant digital transformation at present, driven by the need for improved safety, productivity, and environmental sustainability. With the rapid development of geological exploration and data acquisition technologies, coal mines are generating massive amounts of spatiotemporal data. These data streams, combined with the growing capabilities of artificial intelligence, machine learning, and simulation modeling, offer unprecedented opportunities to understand complex underground processes, predict hazardous events, and optimize mining operations. The integration of data-driven methods into traditional mining engineering practices is becoming a vital strategy for achieving smart and sustainable coal mining.

This Special Issue aims to gather advancements on data-driven analysis, intelligent simulations, and computational modeling of coal mining processes. We welcome interdisciplinary contributions that address theoretical innovations, practical implementations, and case studies of data-centric technologies for understanding and improving coal mine operations. Emphasis will be placed on novel approaches that integrate domain knowledge with experimental and numerical simulation, machine learning, and AI technologies to solve real-world problems in coal mining.

Topics of interest for publication include, but are not limited to, the following:

  • Data mining and machine learning for coal mine monitoring and control;
  • Data-driven analytics of geophysical, geochemical, and drilling exploration of coal mines;
  • Experimental and numerical simulations of underground mining processes;
  • Predictive analytics for mine safety and hazard assessment and treatment;
  • Digital twins and real-time simulation in coal mining;
  • Integration of sensor networks and big data analytics in coal mining;
  • Intelligent decision-making systems for mining operations;
  • Hybrid data-driven and physics-based modeling approaches in coal mines;
  • AI-based modeling of ventilation, water inrush, and gas control in coal mines.

Dr. Huichao Yin
Prof. Dr. Huiqing Lian
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. Processes 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

  • coal mining
  • data-driven modeling
  • machine learning
  • mining simulations
  • mining safety and hazard prediction
  • spatiotemporal data analysis
  • AI in mining operations

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Published Papers (1 paper)

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Research

15 pages, 1869 KiB  
Article
Application of Hybrid Model Based on LASSO-SMOTE-BO-SVM to Lithology Identification During Drilling
by Hui Yao, Manyu Liang, Shangxian Yin, Qing Zhang, Yunlei Tian, Guoan Wang, Enke Hou, Huiqing Lian, Jinfu Zhang and Chuanshi Wu
Processes 2025, 13(7), 2038; https://doi.org/10.3390/pr13072038 - 27 Jun 2025
Viewed by 356
Abstract
Lithology identification during drilling plays a vital role in geological and geotechnical exploration, as it facilitates the early detection of formation-related hazards and supports the development of optimized mining strategies. Traditional lithology identification research involves problems such as fuzzy indicator characteristics and unbalanced [...] Read more.
Lithology identification during drilling plays a vital role in geological and geotechnical exploration, as it facilitates the early detection of formation-related hazards and supports the development of optimized mining strategies. Traditional lithology identification research involves problems such as fuzzy indicator characteristics and unbalanced sample quantities, which affect the accuracy and interpretability of model identification. In order to solve these problems, the Shanxi Guoqiang Coal Mine was taken as the research object, and a combined machine learning model was used to conduct a study on lithology identification during drilling. First, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen the independent variables and retain the parameters that contributed the most to lithology identification. Then, the synthetic minority oversampling technique (SMOTE) algorithm was used to expand the data samples, increase the amounts of minority sample data, and keep the ratios of various lithology data at 1:1. Then, the Bayesian optimization (BO) algorithm was used to optimize the penalty factor C and kernel function hyperparameter γ—two important parameters of the support vector machine (SVM) model—and the BO-SVM lithology identification model was established. Finally, the data samples were processed, and the results were compared with those of single models and unbalanced sample processing to evaluate their effect. The results showed the following: during the drilling process, the four indicators of drilling speed, mud pressure, slurry flow rate, and torque are strongly correlated with the lithology and can be used for lithology identification and classification research. After the data set was oversampled using the SMOTE algorithm, each model had better robustness and generalization ability; the classification result evaluation indicators were also greatly improved, especially for the random forest model, which had a poor original evaluation effect. The BO algorithm was used to optimize the parameters of the SVM model and establish a combined model that correctly identified 95 groups of data out of 96 groups of test samples with an identification accuracy rate of 99%, which was better than that of the traditional machine learning model. The evaluation results were compared with measured data, which confirmed the reliability of the combined model classification method and its potential to be extended to lithology identification and classification work. Full article
(This article belongs to the Special Issue Data-Driven Analysis and Simulation of Coal Mining)
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