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Safety Monitoring and Intelligent Diagnosis of Mining Processes

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 3378

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Special Issue Editors

Dr. Biao Xie

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Guest Editor

College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Interests: mine safety engineering; occupational health and safety; disaster prevention and emergency rescue

Dr. Dexing Li

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Guest Editor

School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: mining safety; coal and rock dynamic disasters; safety monitoring and early warning
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Special Issue Information

Dear Colleagues,

This Special Issue of Processes is dedicated to Safety Monitoring and Intelligent Diagnosis of Mining Processes. Processes is a peer-reviewed scientific journal that publishes articles and communications in the interdisciplinary area of mining safety. For detailed information on the journal, we refer you to the following: https://www.mdpi.com/journal/processes.

Mining processes involve complex operations like blasting, excavation, and transportation, which often entail potential safety risks such as gas leakage, equipment failure, and slope instability. Traditional monitoring methods have limitations in terms of the real-time and accurate detection of these risks. Safety monitoring and intelligent diagnosis of mining processes utilize advanced sensors to collect real-time data on parameters such as gas concentration, vibration, and temperature. Then, intelligent algorithms analyze these data to enable early warning of safety hazards and accurate diagnosis of equipment faults. This approach not only enhances the safety of mining operations but also reduces downtime and improves efficiency. Its development and application have become key directions for the modernization of the mining industry, holding significant implications for the sustainable development of the sector.

This Special Issue is open for the submission of papers focused on subject areas related to Safety Monitoring and Intelligent Diagnosis of Mining Processes. The listed keywords suggest just a few topics of interest, though we welcome many other possibilities.

Dr. Biao Xie
Dr. Dexing Li
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 250 words) can be sent to the Editorial Office for assessment.

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 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

mine fire detection
mine water inrush warning
gas disaster monitoring and prevention
mine dust control
strata pressure behavior analysis
mine intelligent ventilation
microseismic monitoring technology
real-time risk assessment
disaster evolution simulation
intelligent fault diagnosis

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Published Papers (6 papers)

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Research

19 pages, 7513 KB

Open AccessArticle

Study on the Top Coal Recovery Behavior and Parameter Optimization Under Different Caving Ratios in Thick Coal Seam Mining

by Jiantao Cao, Wen Zhang, Xingping Lai, Shuai Zhang, Chang Xin, Feilong Xin and Lizheng Xu

Processes 2026, 14(5), 776; https://doi.org/10.3390/pr14050776 - 27 Feb 2026

Viewed by 339

Abstract

Longwall top coal caving is one of the most effective methods for extracting steeply inclined and ultra-thick coal seams. To investigate the influence of caving ratio (the proportion between mining height and top coal thickness) on top coal recovery behavior and ground pressure characteristics, this study employs both the Particle Flow Code (PFC) discrete element method and a coupled FLAC^3D–PFC^3D numerical simulation approach. The effects of different caving ratios (1:3, 1:3.2, and 1:3.4) on the top coal recovery ratio, stress distribution, and gangue accumulation characteristics were analyzed. The results show that the caving ratio has a significant impact on top coal recovery. At a caving ratio of 1:3.2, adopting a two-cut-one-cave interval resulted in a top coal recovery ratio as high as 94.8%. A stress-relief zone with an arch-like distribution formed above the goaf, while a stress concentration zone developed ahead of the coal wall, where the coal–rock mass underwent compression and failure. The roof displacement exhibited an arch-shaped distribution, while the floor displacement was asymmetrical, with greater heaving observed at the lower end. As the working face advanced, the horizontal development of the plastic zone expanded rapidly, while the vertical extent changed only slightly. Throughout the caving process, the top coal demonstrated favorable caving behavior with good flowability and accumulation characteristics. These findings provide theoretical support for achieving high mining recovery in thick coal seam operations and offer practical guidance for optimizing caving process parameters in practice. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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25 pages, 4622 KB

Open AccessArticle

Discrete Symbiotic Organisms Search with Adaptive Mutation for Simultaneous Optimization of Features and Hyperparameters and Its Application

by Nan Zeng, Xingdong Zhao and Yi Duan

Processes 2026, 14(2), 320; https://doi.org/10.3390/pr14020320 - 16 Jan 2026

Viewed by 391

Abstract

Effective engineering modeling requires simultaneously addressing feature selection and hyperparameter interdependence, a challenge exacerbated by high-dimensional data characteristics in complex engineering modeling. Traditional optimization methods typically address these two aspects separately, which limits overall model performance. This study introduces a hybrid framework to enhance the performance of extreme gradient boosting (XGBoost) in engineering applications. The framework comprises two main phases: first, preliminary feature selection guided by prior domain knowledge and statistical analysis to reduce data dimensionality while preserving interpretability; second, a discrete symbiotic organisms search algorithm with adaptive feature mutation (DMSOS) simultaneously optimizes feature subsets and XGBoost hyperparameters. The DMSOS employs a discretization strategy to separate feature selection from hyperparameter tuning, facilitating focused searches within distinct spaces. An adaptive mutation mechanism dynamically adjusts exploration intensity based on iteration progress and feature importance. Additionally, evaluations on 1414 field-measured blasting vibration data demonstrate that the proposed DMSOS-XGBoost model achieves superior prediction performance, with an r² of 0.96696 and RMSE of 0.02636, outperforming models optimized via traditional sequential approaches. Further interpretability analysis highlights spatial geometry and explosive load as critical features, offering actionable insights for environmental risk management. This research provides a valuable methodological reference for engineering modeling scenarios requiring simultaneous optimization of features and hyperparameters. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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16 pages, 5921 KB

Open AccessArticle

Discrete Element Simulation Study on Direct Shear Mechanical Behavior of Coal Under the Influence of Bedding Angle

by Jinhong Hu, Jianchun Ou, Xiaojun He, Bican Wang and Yanjun Tong

Processes 2025, 13(12), 4044; https://doi.org/10.3390/pr13124044 - 14 Dec 2025

Viewed by 481

Abstract

Bedding angles (BAs) in coal mining promote shear failure and can trigger rockbursts. Using Particle Flow Code (PFC) direct shear simulations on coal with BA = 0°, 30°, 60°, and 90°, we quantified BA effects on mechanical behavior and cracking. Increasing BA reduces shear strength and shear modules, reaching minimum of 4 MPa and 0.7 GPa at 90°. Failure modes shift from progressive, bedding parallel shearing at 0° to mixed paths at 30–60°, and abrupt brittle failure at 90°. Crack density and orientation evolve systematically: dense bedding parallel shear at 0°; more dispersed, lower-density mixed shear tension at 30–60°; and reconcentrated, high-density cracking causing premature shear at 90°. Corresponding force chain patterns aligned at 0°, dispersed at 30–60°, and realigned at 90° govern these outcomes by modulating stress transfer across bedding interfaces. Overall, BA is the first-order control on coal shear instability; the quantified thresholds and mechanisms provide actionable guidance for excavation orientation, support design, and targeted monitoring to reduce shear out and rockburst risks in coal mines. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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28 pages, 3604 KB

Open AccessArticle

Intelligent Early Warning and Sustainable Engineering Prevention for Coal Mine Shaft Rupture

by Qiukai Gai, Gang Yang, Qingli Liu, Qiang Fu, Shiqi Liu, Qing Ma and Chao Lian

Processes 2025, 13(12), 4016; https://doi.org/10.3390/pr13124016 - 12 Dec 2025

Viewed by 522

Abstract

Shaft lifting is an important process of coal mining, and its integrity is a prerequisite for ensuring efficient mining. The non-mining-induced rupture of vertical shafts in coal mines, primarily caused by the consolidation settlement of overlying unconsolidated strata due to aquifer dewatering, poses a significant threat to mining safety. Accurately predicting such ruptures remains challenging due to the multicollinearity and complex interactions among multiple influencing factors. This study proposes a novel multiscale discriminant analysis model, termed the SDA-PCA-FDA model, which integrates Stepwise Discriminant Analysis (SDA), Principal Component Analysis (PCA), and Fisher’s Discriminant Analysis (FDA). Initially, SDA screened five principal controlling factors from nine original variables. Subsequently, PCA was applied to reorganize these factors into three principal components, effectively eliminating information redundancy. Finally, the FDA model was established based on these components. Validation results demonstrated that the SDA-PCA-FDA model achieved high correct classification rates of 96.43% and 91.67% on the training and testing sets, respectively, significantly outperforming traditional FDA, PCA-FDA, and SDA-FDA models. Applied to engineering practice in the Yanzhou Mining Area, the model successfully predicted the rupture risk of the main shaft, consistent with field observations. Furthermore, to achieve sustainable governance, the “Friction Pile Method” was proposed as a preventive measure. Numerical simulations using NM2dc software determined the optimal governance parameters: a pile height of 112.86 m, a stiffness coefficient of 0.9, and a pile–shaft spacing of 10 m. A comparative analysis incorporating techno-economic sustainability indicators confirmed the superior effectiveness and economic viability of the friction pile method over traditional approaches. This research provides a reliable, multiscale methodology for both the prediction and sustainable governance of non-mining-induced shaft rupture. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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12 pages, 3062 KB

Open AccessArticle

Discrete Element Simulation Study on Shear Mechanical Properties of Coal Seams with Horizontal Bedding Under Different Normal Stresses

by Xinchuan Fan, Jianchun Ou, Yanjun Tong, Xiaojun He and Bican Wang

Processes 2025, 13(12), 4001; https://doi.org/10.3390/pr13124001 - 11 Dec 2025

Viewed by 492

Abstract

In deep coal mining, fault slip-type rockbursts occur frequently. Understanding the shear mechanical properties of bedded coal seams and their intrinsic mechanisms is crucial. This study used PFC^2D7.0 numerical simulation to systematically investigate the shear mechanical behavior and micro-mechanisms of bedded coal under different normal stresses (1, 2, 3, 4 MPa). The research results show that: (1) The shear stress-displacement curves of bedded coal show three stages: elastic rise, strain softening, and residual stability. Both peak and residual shear strengths increase with the rise in normal stress. The peak strength shows nonlinear growth, while the residual strength exhibits a good linear relationship. Higher normal stress significantly reduces the strength reduction rate and effectively inhibits the brittleness of coal. (2) The failure mode consistently manifests as shear failure along the preset weak bedding plane, forming a distinct shear zone. Crack evolution analysis shows that shear cracks within the bedding are the primary form of damage, with minimal contribution from tensile cracks. (3) Force chain analysis shows that an increase in normal stress significantly enhances the density and connectivity of compressive force chains within the shear zone. It also effectively inhibits tensile force chains, with the bedding plane consistently serving as the primary area for stress concentration and transfer. This study provides important theoretical references for understanding the shear instability mechanism of bedded coal, predicting its mechanical response, and preventing fault slip-type rockbursts in deep coal mines. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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22 pages, 6086 KB

Open AccessArticle

Beyond Static Fingerprints to Dynamic Evolution: A CNN–LSTM–Attention Model for Identifying Coal Mine Water Inrush Sources in Northern China

by Shaobo Yin, Chenglin Chang, Mingwei Zhang, Gang Wang, Qimeng Liu and Qiding Ju

Processes 2025, 13(12), 3906; https://doi.org/10.3390/pr13123906 - 3 Dec 2025

Viewed by 561

Abstract

Mine water inrush poses a severe threat to coal mine safety, making rapid and accurate identification of water sources essential. Existing methods, including conventional hydrochemical diagrams and machine learning, struggle with high-dimensional, nonlinear hydrogeochemical data characterized by implicit temporal dynamics. This study proposes an intelligent identification model integrating convolutional neural networks (CNNs), long short-term memory (LSTM), and an attention mechanism (CNN–LSTM–Attention). The model employs a CNN to extract local fingerprint features from hydrochemical indicators (K⁺+Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻), uses LSTM to model evolutionary patterns, and leverages an attention mechanism to adaptively focus on critical discriminative features. Based on 76 water samples from the Tangjiahui Coal Mine, the model achieved 91% accuracy on the test set, outperforming standalone CNN, LSTM, and CNN–LSTM models. Visualization of attention weights further revealed key diagnostic indicators, enhancing interpretability and bridging data-driven methods with hydrogeochemical mechanisms. This study provides a powerful and interpretable tool for water inrush source identification, supporting the transition toward intelligent and transparent coal mine water hazard prevention. Full article

(This article belongs to the Special Issue Safety Monitoring and Intelligent Diagnosis of Mining Processes)

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