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Minerals
Special Issues
Sustainable Mining: Advancements, Challenges and Future Directions
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Sustainable Mining: Advancements, Challenges and Future Directions

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: 1 November 2025 | Viewed by 4978

Special Issue Editors

Prof. Dr. Jacopo Seccatore

E-Mail Website
Guest Editor
Metallurgy and Mining Engineering Department, Universidad Catolica del Norte, Antofagasta 1270398, Chile
Interests: sustainable small scale mining; mining methods and techniques for small scale underground mining
Prof. Dr. Tatiane Marin

E-Mail Website
Guest Editor
Department of Metallurgical and Materials Engineering, Universidad Técnica Federico Santa Maria, Santiago, Chille
Interests: small scale mining; pine planning applied to small scale mining

Special Issue Information

Dear Colleagues,

Sustainable mining practices are the future of the extractive industry. The challenges are technical and extra-technical. The large-scale mines of the future will always deal with lower mineral grades and deeper deposits, facing issues with technical viability and the safety and well-being of the operators. The deep deposits will provide stronger rocks, which will increase the energy demand for the comminution processes: the sources for this energy will have to shift to clean and renewable. On the other hand, the new deposits available are rich in grade,  close to the surface and are of a small scale. The artisanal and small-scale mining will take a larger and larger role in the mineral value chain, facing the environmental and social issues that this size of mining carries. The value chain will shift from a linear to a circular economy, which will include the objective of zero-waste mining.

This Special Issue focuses on the advancements, challenges, and future directions of sustainable mining. We invite original research articles, reviews, and other related contributions to this Special Issue.

Prof. Dr. Jacopo Seccatore
Prof. Dr. Tatiane Marin
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. Minerals 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.

Keywords

  • small scale mining
  • zero waste mining
  • mine closure
  • low mineral grades
  • great depth mining
  • circular economy of minerals

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

23 pages, 14082 KiB  
Article
Procedure Design and Reliability Analysis for Prediction of Surface Subsidence of a Metal Mine Induced by Block Caving Method—A Case Study of Pulang Copper Mine in China
by Weijia Ling, Zhonghua Zhu, Xinglong Feng, Liguan Wang, Weixiong Wang, Zhengrong Li and Jiadong Qiu
Minerals 2024, 14(10), 1011; https://doi.org/10.3390/min14101011 - 7 Oct 2024
Viewed by 1209
Abstract
Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars [...] Read more.
Surface subsidence resulting from block caving mining causes considerable environmental and economic harm in mining areas, highlighting the critical need for accurate predictions of surface subsidence. Given the unique features of the block caving technique and the resemblance between the released ore pillars and the mining processes, this paper developed a lightweight model to forecast surface settlement utilizing the probability integration approach to address the issue of predicting surface settlement in metallic mines. This study focuses on the Pulang Copper Mine, situated in the northeast of Shangri-La County within the Yunnan Province, as a case example. This mine employs the block caving method, which results in substantial surface subsidence. A visual mining simulation program is designed to combine the ore mining plan with the prediction model, manage the ore output of each mining point in batches, treat the ore pillars released in the planning cycle as strip work, and simulate and calculate the surface area above the ore pillars settlement value. The calculated values of surface subsidence induced by ore drawing are then interpreted as the downward displacement of the surface subsidence beneath the strip workings. Furthermore, to verify the reliability of the model, three-dimensional laser point cloud data of the Pulang Copper Mine in recent years were collected, and the differences between the predicted surface and the measured surface were calculated and analyzed. Full article
(This article belongs to the Special Issue Sustainable Mining: Advancements, Challenges and Future Directions)
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20 pages, 4685 KiB  
Article
Causal Analysis of Roof Caving on Underground Mine: A New Theory and Optimized DEMATEL Approach
by Zhenhang Xiao, Fuding Mei and Chuanyu Hu
Minerals 2024, 14(10), 992; https://doi.org/10.3390/min14100992 - 30 Sep 2024
Viewed by 832
Abstract
In the context of mines, roof-caving incidents constitute the most common and expensive accidents. To enhance the management and prevention of roof-caving accidents, it is imperative to investigate the factors that contribute to such incidents and comprehend the intricate causal relationships among them. [...] Read more.
In the context of mines, roof-caving incidents constitute the most common and expensive accidents. To enhance the management and prevention of roof-caving accidents, it is imperative to investigate the factors that contribute to such incidents and comprehend the intricate causal relationships among them. This study aims to classify the causes of these accidents into three categories: basic factors, controllable factors, and sudden factors, based on the mechanism of roof caving. The categorization is primarily determined by two indicators: intervisibility and variability. Furthermore, the study delves into analyzing the mutual influence relationships among these factors and proposes the BCX theory (Basic-Controllable-Sudden causing theory) for roof caving. Subsequently, based on this theory, an index system called BCX is established for roof caving, and the DEMATEL method is employed to analyze the factors within this index system. To attain more accurate results, this study utilizes interval trapezoidal type-2 fuzzy number scale optimization and Tsallis relative entropy to address the limitations of the DEMATEL method. By comparing the outcomes of the traditional and optimal DEMATEL methods, it is observed that the optimal method exhibits superior applicability in the BCX index system of roof caving, with results that align closely with the actual scenario. Therefore, the optimal DEMATEL method’s analysis of centrality, importance, and chain relationships between the factors within the BCX index system will offer valuable guidance for preventing roof-caving accidents in mining operations. Full article
(This article belongs to the Special Issue Sustainable Mining: Advancements, Challenges and Future Directions)
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24 pages, 48550 KiB  
Article
Identification of Rock Fragments after Blasting by Using Deep Learning-Based Segment Anything Model
by Junjie Zhao, Diyuan Li and Yisong Yu
Minerals 2024, 14(7), 654; https://doi.org/10.3390/min14070654 - 26 Jun 2024
Cited by 2 | Viewed by 2090
Abstract
Rock fragmentation is an important evaluation indicator for field blasting operations. This paper applies a deep learning-based method, the Segment Anything Model (SAM), to automatically segment rock fragments. To review the SAM’s segmentation performance, 83 images of rock fragment collected from the mine [...] Read more.
Rock fragmentation is an important evaluation indicator for field blasting operations. This paper applies a deep learning-based method, the Segment Anything Model (SAM), to automatically segment rock fragments. To review the SAM’s segmentation performance, 83 images of rock fragment collected from the mine site were used as the test dataset. Pixel-level accuracy (PA), intersection over union (IOU), and dice coefficient (Dice) were employed to evaluate the model pixel-level segmentation performance. The results showed that the SAM exhibited excellent segmentation performance on the test data (PA = 94.5%, IOU = 94.4%, Dice = 95.4%). The coefficient of determination (R2) values for the 50% and 80% passing sizes (X50 and X80) were 0.970 and 0.991, respectively, which demonstrated that the SAM could achieve high precision measurement of rock fragmentation. Additionally, the effectiveness of the SAM was further evaluated by comparing it to commercial software, and the generalizability of the SAM was verified on two other datasets. The findings revealed that the SAM not only outperformed the Split-Desktop V 4.0 on the test dataset but also achieved comparable accuracy to previous studies on the two other datasets. The SAM could be regarded as a useful tool to provide fast and accurate feedback for field blasting. Full article
(This article belongs to the Special Issue Sustainable Mining: Advancements, Challenges and Future Directions)
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