Data-Driven Analysis and Simulation of Coal Mining

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

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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.

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