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	19.8 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Minerals minerals	2.2	4.4	2011	18.2 Days	CHF 2400	Submit
Mining mining	-	4.0	2021	19.3 Days	CHF 1200	Submit
Processes processes	2.8	5.5	2013	16 Days	CHF 2400	Submit

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Published Papers (3 papers)

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All Applied Sciences Energies Minerals Mining Processes

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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

Viewed by 226

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

Viewed by 326

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 331

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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