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Green Mining: Theory, Methods, Computation and Application
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Green Mining: Theory, Methods, Computation and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 805

Special Issue Editors

Prof. Dr. Kang Peng

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: intelligent mining; risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Song Luo

E-Mail Website
Guest Editor
School of Resources Environment and Safety Engineering, University of South China, Hengyang 421001, China
Interests: mining disaster control; deep rock mechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Quanle Zou

E-Mail Website
Guest Editor
College of Resources and Safety, Chongqing University, Chongqing, 400030, China
Interests: mine gas disaster prevention and control; coalbed methane development

Special Issue Information

Dear Colleagues,

With the increasing tension between resource development and environmental protection, green mining has emerged as an innovative and sustainable mining strategy, becoming a significant research topic in the field of mining engineering. This Special Issue, entitled "Green Mining: Theory, Methods, Computation, and Application," aims to delve into the development of green mining and promote the integration of theoretical foundations with practical applications.

We welcome the submission of research articles whose scope includes, but is not limited to, the following topics:

(1) Green mining theory and technology; 

(2) Intelligent mining theory and methods; 

(3) Intelligent backfill mining theory and technology; 

(4) Deep rock mechanics and engineering; 

(5) Resource recovery technology; 

(6) Mine safety science and engineering.

In addition, this Special Issue encourages the presentation of case studies to showcase best practices and innovative achievements within the industry.

By gathering multidisciplinary research, this Special Issue aims to deepen our understanding of green mining, providing scientific evidence and technical support for the sustainable development and utilization of resources.

Prof. Dr. Kang Peng
Dr. Song Luo
Dr. Quanle Zou
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. Applied Sciences 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

  • green mining
  • intelligent mining
  • deep rock mechanics
  • backfill mining
  • machine learning and artificial intelligence
  • backfill mining

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Published Papers (1 paper)

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Research

35 pages, 30272 KiB  
Article
Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China
by Yuhao Chen, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei and Mingyuan Zhang
Appl. Sci. 2025, 15(8), 4082; https://doi.org/10.3390/app15084082 - 8 Apr 2025
Viewed by 556
Abstract
With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion [...] Read more.
With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article
(This article belongs to the Special Issue Green Mining: Theory, Methods, Computation and Application)
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