Green Mining, 3rd Edition

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Green Mining, https://www.mdpi.com/topics/G09CLY8PTX”. Mining is a basic industry which helps to advance social development and national economic construction. In the overall process of mineral resource exploration and development, a scientific and orderly form of mining is implemented where disturbance to the ecological environment around the mining area is managed within a controllable range. It is therefore of great significance within mining to realize environmental ecology, scientific mining methods, efficient utilization of resources, digitization of management information, and harmony between mining communities.

This research Topic aims to provide a platform for new research and recent advances in green mining technology. To promote the development of green mine construction, we encourage the submission of high-quality original research papers, including, but not limited to, the following topics:

• Safe and sustainable mining;
• Mineral resource management;
• Intelligent mining technology;
• Mining equipment;
• Geomechanics and geophysics;
• Rehabilitation of mine sites;
• Human–machine–environment systems;
• Green exploration in mines;
• Mine safety and personnel health;
• Harmless treatment of solid waste in mines.

Prof. Dr. Kun Du
Dr. Jianping Sun
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Minerals minerals	2.2	4.4	2011	17.7 Days	CHF 2400	Submit
Mining mining	-	4.0	2021	22.7 Days	CHF 1200	Submit
Processes processes	2.8	5.5	2013	14.9 Days	CHF 2400	Submit

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Published Papers (7 papers)

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17 pages, 4956 KB  

Open AccessArticle

Load Characteristics in the Cutting of Three Types of Bauxite by Conical Picks

by Weipeng Xu, Bin Zhang, Nangeng Yue, Kuidong Gao, Ziyao Ma, Shengru Zhang, Xiaodi Zhang and Yu Bu

Appl. Sci. 2026, 16(6), 2695; https://doi.org/10.3390/app16062695 - 11 Mar 2026

Viewed by 327

Abstract

Underground bauxite comprehensive mechanized mining has attracted growing attention, yet regional differences in bauxite characteristics challenge its applicability and economic efficiency. The conical pick, a key cutting tool for this mining method, has cutting load characteristics that directly impact the cutting mechanism’s efficiency [...] Read more.

Underground bauxite comprehensive mechanized mining has attracted growing attention, yet regional differences in bauxite characteristics challenge its applicability and economic efficiency. The conical pick, a key cutting tool for this mining method, has cutting load characteristics that directly impact the cutting mechanism’s efficiency and reliability. Three typical bauxite samples from different mining areas were selected as research objects. After testing their composition and firmness coefficients, a linear cutting test bench was used to measure their tri-axial cutting forces at various cutting depths. Regression analysis and the linear fitting of cutting coefficients were conducted to study cutting force and normal force (two core cutting loads). The results show that power functions effectively describe the relationships between the cutting force, normal force, fluctuation coefficient and cutting depth. As the bauxite firmness coefficient and cutting depth rise, the cutting force peak value, fluctuation amplitude and frequency all increase. Excessively high or low cutting coefficients reduce the cutting efficiency; only when both the cutting coefficient and the depth are in an optimal range can the cutting efficiency reach its maximum. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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15 pages, 3569 KB  

Open AccessArticle

Research and Application of Intelligent Ventilation Management System for Maping Phosphate Mine

by Long Zhang, Zhujun Zha and Zunqun Xiao

Appl. Sci. 2026, 16(2), 715; https://doi.org/10.3390/app16020715 - 9 Jan 2026

Viewed by 495

Abstract

The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on [...] Read more.

The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on the Python PyQt5 library was developed for Maping Phosphate Mine to improve ventilation efficiency, lower dust concentration at the working face, and enhance safety by addressing uneven air volume distribution. The implementation of an integrated system, comprising a 3D ventilation network model, remote control capabilities, and smart algorithms, has successfully realized zonal planning and on-demand ventilation in the mine’s underground workings. To adapt to the fluctuating air demand at the tunneling face, a remote intelligent control scheme for louvered dampers was implemented. This dynamic demand-based strategy achieves precise distribution of air volume throughout the ventilation network. The research results demonstrate that the system effectively addresses the uneven distribution of air volume, thereby improving the overall ventilation environment and reducing the risk of ventilation-related accidents. The system serves dual purposes: it provides an intelligent ventilation control mechanism and integrates seamlessly with the key subsystems for underground safety production. This synergy is instrumental in advancing the mine’s digitalization and intelligent transformation initiatives. Field test results indicate that the system achieved a 30% reduction in energy consumption and a 70% decrease in dust concentration at the working face, respectively. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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23 pages, 3957 KB  

Open AccessArticle

CFD Investigation of Gas–Liquid Two-Phase Flow Dynamics and Pressure Loss at Fracture Junctions for Coalbed Methane Extraction Optimization

by Xiaohu Zhang, Mi Li, Aizhong Luo and Jiong Wang

Processes 2026, 14(1), 69; https://doi.org/10.3390/pr14010069 - 24 Dec 2025

Viewed by 420

Abstract

The dynamics of gas–liquid two-phase flow at fracture junctions are crucial for optimizing fluid transport in the complex fracture networks of coal seams, particularly for coalbed methane (CBM) extraction and gas hazard management. This study presents a comprehensive numerical investigation of transient air–water [...] Read more.

The dynamics of gas–liquid two-phase flow at fracture junctions are crucial for optimizing fluid transport in the complex fracture networks of coal seams, particularly for coalbed methane (CBM) extraction and gas hazard management. This study presents a comprehensive numerical investigation of transient air–water flow in a two-dimensional, symmetric, cross-shaped fracture junction. Using the Volume of Fluid (VOF) model coupled with the SST k-ω turbulence model, the simulations accurately capture phase interface evolution, accounting for surface tension and a 50° contact angle. The effects of inlet velocity (0.2 to 5.0 m/s) on flow patterns, pressure distribution, and energy dissipation are systematically analyzed. Three distinct phenomenological flow regimes are identified based on interface morphology and force balance: an inertia-dominated high-speed impinging flow (Re > 4000), a moderate-speed transitional flow characterized by a dynamic balance between inertial and viscous forces (∼1000 < Re < ∼4000), and a viscous-gravity dominated low-speed creeping filling flow (Re < ∼1000). Flow partitioning at the junction—defined as the quantitative split of the total inflow between the main (straight-through) flow path and the deflected (lateral) paths—exhibits a strong dependence on the Reynolds number. The main flow ratio increases dramatically from approximately 30% at Re ∼ 500 to over 95% at Re ∼ 12,000, while the deflected flow ratio correspondingly decreases. Furthermore, the pressure loss (head loss, ΔH) across the junction increases non-linearly, following a quadratic scaling relationship with the inlet velocity (ΔH ∝ V₀^1.95), indicating that energy dissipation is predominantly governed by inertial effects. These findings provide fundamental, quantitative insights into two-phase flow behavior at fracture intersections and offer data-driven guidance for optimizing injection strategies in CBM engineering. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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17 pages, 2380 KB  

Open AccessArticle

Process Optimization and Simulation of Ventilation Systems in Multi-Mining Areas Using TOPSIS at Maping Phosphate Mine

by Long Zhang, Zhujun Zha and Zunqun Xiao

Processes 2025, 13(12), 4034; https://doi.org/10.3390/pr13124034 - 13 Dec 2025

Viewed by 580

Abstract

Maping Phosphate Mine operates as a large-scale mining complex characterized by a multi-mining area strip mining layout. This configuration exhibits expansive operational zones, numerous dispersed mining sites, and inherent systemic complexity, collectively complicating ventilation system management. The optimization of ventilation processes across multiple [...] Read more.

Maping Phosphate Mine operates as a large-scale mining complex characterized by a multi-mining area strip mining layout. This configuration exhibits expansive operational zones, numerous dispersed mining sites, and inherent systemic complexity, collectively complicating ventilation system management. The optimization of ventilation processes across multiple mining areas constitutes a critical measure for enhancing operational safety and efficiency within resource-constrained scenarios. This investigation specifically targets four adjacent mining zones—340B, 380B, 380C, and 420D—where three distinct ventilation schemes were formulated and evaluated. A process-oriented simulation-optimization model combining Ventsim and TOPSIS was developed to evaluate the ventilation systems. The ventilation network architecture and airflow distribution characteristics of the target mining areas were comprehensively simulated, establishing a decision optimization framework for the ventilation system that successfully identified the optimal solution. The results demonstrate minimal error between the simulated and measured data of the mine ventilation network model, validating the accuracy of its system parameter estimations. Simulations of diverse ventilation schemes generated airflow distribution parameters and dust concentration data for each mining area. Subsequently, a TOPSIS-integrated process optimization model was developed to comprehensively evaluate the ventilation schemes against eight quantitative indicators. Evaluation results identified Scheme Two as the optimal solution, as it demonstrates a balanced optimization of safety, efficiency, and cost-effectiveness. This scheme achieves a significant enhancement of the underground ventilation environment and a marked suppression of dust diffusion, with only a marginal increase in overall ventilation costs. By elevating the air volume from an initial less than 1.0 m³/s to a precisely regulated range of 5.0–13.0 m³/s, the scheme fundamentally eliminated ventilation dead zones. This intervention resulted in a significant reduction in dust concentrations across multiple working faces, consistently maintaining levels below the 4 mg/m³ national exposure limit (GBZ 2.1-2019), and ultimately ensured a safer and healthier working environment. The attainment of these practical outcomes, which directly correspond to the optimization objectives of the TOPSIS method, confirms its efficacy and practical value in guiding ventilation strategy selection. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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21 pages, 1511 KB  

Open AccessArticle

Research on Intelligent Early Warning and Emergency Response Mechanism for Tunneling Face Gas Concentration Based on an Improved KAN-iTransformer

by Lei An, Shaoqi Kong and Kunjie Li

Processes 2025, 13(11), 3748; https://doi.org/10.3390/pr13113748 - 20 Nov 2025

Cited by 1 | Viewed by 635

Abstract

The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial [...] Read more.

The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial for ensuring the safety of coal mining operations. This study introduces a novel approach for gas concentration forecasting at the tunneling face by integrating the Kolmogorov–Arnold Network (KAN) with an enhanced iTransformer model, leveraging multi-source sensor data for enhanced predictive capabilities. KAN improves the feature extraction ability due to flexible mapping kernel function that is capable of capturing complicated nonlinearities between gas emission volume and environmental variables. iTransformer, with concentrated attention mechanism and sparsity pattern, can further model very long-term sequence dependencies and learn to capture multi-scale features. As a whole, they address the problem of gradient vanishing and insufficient feature extraction in the temporal sequential prediction models based on deep learning methods with long sequences input, leading to significant improvements in prediction accuracy and model stability. Experiments on site monitoring datasets demonstrate that the proposed KAN + iTransformer model achieved better fitting and generalization capacity than two baseline models (iTransformer, Transformer) for gas concentration prediction. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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24 pages, 8707 KB  

Open AccessArticle

Multiphysical Coupling Analysis of Sealing Performance of Underground Lined Caverns for Hydrogen Storage

by Shaodong Cui, Yin Li, Junwu Zou and Yun Chen

Processes 2025, 13(11), 3716; https://doi.org/10.3390/pr13113716 - 18 Nov 2025

Cited by 1 | Viewed by 745

Abstract

The accurate analysis of the sealing performance of underground lined cavern hydrogen storage is critical for enhancing the stability and economic viability of storage facilities. This study conducts an innovative investigation into hydrogen leakage behavior by developing a multiphysical coupled model for a [...] Read more.

The accurate analysis of the sealing performance of underground lined cavern hydrogen storage is critical for enhancing the stability and economic viability of storage facilities. This study conducts an innovative investigation into hydrogen leakage behavior by developing a multiphysical coupled model for a composite system of support structures and surrounding rock in the operation process. By integrating Fick’s first law with the steady-state gas permeation equation, the gas leakage rates of stainless steel and polymer sealing layers are quantified, respectively. The Arrhenius equation is employed to characterize the effects of temperature on hydrogen permeability and the evolution of gas permeability. Thermalmechanical coupled effects across different materials within the storage system are further considered to accurately capture the hydrogen leakage process. The reliability of the established model is validated against analytical solutions and operational data from a real underground compressed air storage facility. The applicability of four materials—stainless steel, epoxy resin (EP), ethylene–vinyl alcohol copolymer (EVOH), and polyimide (PI)—as sealing layers in underground hydrogen storage caverns is evaluated, and the influences of four operational parameters (initial temperature, initial pressure, hydrogen injection temperature, and injection–production rate) on sealing layer performance are also systematically investigated. The results indicate that all four materials satisfy the required sealing performance standards, with stainless steel and EP demonstrating superior sealing performance. The initial temperature of the storage and the injection temperature of hydrogen significantly affect the circumferential stress in the sealing layer—a 10 K increase in initial temperature leads to an 11% rise in circumferential stress, while a 10 K increase in injection temperature results in a 10% increase. In addition, the initial storage pressure and the hydrogen injection rate exhibit a considerable influence on airtightness—a 1 MPa increase in initial pressure raises the leakage rate by 11%, and a 20 kg/s increase in injection rate leads to a 12% increase in leakage. This study provides a theoretical foundation for sealing material selection and parameter optimization in practical engineering applications of underground lined caverns for hydrogen storage. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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17 pages, 994 KB  

Open AccessArticle

Dynamic Escape Path Optimization Model Study Based on Spatio-Temporal Evolution of Coal Mine Water Inrush

by Lin An, Zaibing Liu, Xinmiao Wang, Wenming Liu, Shaolong Wang, Liang Ma, Tao Fan, Weiming Chen and Junjie Hu

Processes 2025, 13(11), 3666; https://doi.org/10.3390/pr13113666 - 12 Nov 2025

Viewed by 646

Abstract

To reduce the risk of coal mine water inrush, a dynamic escape path optimization model based on the spatio-temporal evolution of the water inrush is studied. The actual coal mine is simplified into roadway nodes and segments to meet the real-time simulation of [...] Read more.

To reduce the risk of coal mine water inrush, a dynamic escape path optimization model based on the spatio-temporal evolution of the water inrush is studied. The actual coal mine is simplified into roadway nodes and segments to meet the real-time simulation of the coal mine water inrush, where the computational cost is reduced significantly while the accuracy is acceptable. To solve the control equations of the open channel flow and full channel flow efficiently, the lattice Boltzmann method is adopted to simulate the spatio-temporal evolution of the water inrush. Different from the previous studies, the spatio-temporal evolution of the water inrush is taken into account, which is closer to the actual case. The escape speed is not static, which is affected by the water depth dynamically; meanwhile, the effect of the physical energy reduction is considered. To validate the dynamic escape path optimization model based on the spatio-temporal evolution of the coal mine water inrush, three case studies are conducted. In the first case, there is one water inrush point and one person, while in the second case, there are two water inrush points and four persons; the third case is an actual coal mine with multiple water inrush points. We defined two indicators to evaluate the risk of the escape path quantitatively; they are the window escape time and rescue priority. By conducting the dynamic programming of the escape path, the optimal escape path is selected, where the effectiveness of the dynamic escape path optimization model is validated. The present work is helpful in reducing the risk of coal mine water inrush and improving the safety of the early warning system. Full article

(This article belongs to the Topic Green Mining, 3rd Edition)

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