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Advances in Intelligent and Sustainable Mining

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 46514

Special Issue Editors

Dr. Pingan Peng

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: intelligent mining; unmanned mining
Dr. Xianyang Qiu

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: sustainable mining; intelligent blasting in mining
Dr. Qiusong Chen

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: groundwater friendly backfilling; comprehensive utilization of solid waste from mining, dressing and smelting
Special Issues, Collections and Topics in MDPI journals
Dr. Liguan Wang

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: intelligent mining

Special Issue Information

Dear Colleagues,

Mining is the foundation of global economic development. The development of the modern mining industry faces a series of problems, such as harsh mining conditions, high safety risks, and insufficient labor. COVID-19 has moved up the timeline for the modernization programs of mining industry, including digitization and automation. To protect worker health and safety, enterprises have fast-tracked technologies to reduce the number of people onsite and increase the ability to operate mines remotely. Therefore, the establishment of intelligent, green, and sustainable mines has gradually become a consensus in the industry. Recent years have witnessed rapid progress in intelligent mining. Cutting-edge technologies such as the internet of things, big data, artificial intelligence, 5G, edge computing, and virtual reality have greatly promoted the intelligent development of the mining industry, and comprehensively improved the sustainable level of mining. The global mining industry is going through a new revolution.

The present Special Issue is aimed to collect the innovative achievements in different perspectives of Intelligent and Sustainable  Mining. The key areas include, but are not limited to:

1 Theoretical and technical progress on green, low-carbon, intelligent mine construction;

2 Intelligent blasting technology and hazard control;

3 Advanced minimisation strategies during mining waste generation;

4 Innovative development about cemented paste backfill, including theory and technology related backfill aggregates, cementitious materials, auxiliary additives;

5 Self-driving technology used in mines;

6 Application of artificial intelligence (AI) in mining;

7 Intelligent monitoring for mining safety;

8 Related case presentations.

Dr. Pingan Peng
Dr. Xianyang Qiu
Dr. Qiusong Chen
Dr. Liguan Wang
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. Sustainability 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

  • sustainable mining
  • intelligent mining
  • intelligent blasting
  • mining solid waste
  • cemented paste backfill self-driving
  • microseismic monitoring
  • artificial intelligence

Published Papers (28 papers)

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16 pages, 4358 KiB  
Article
Algorithm for Point Cloud Dust Filtering of LiDAR for Autonomous Vehicles in Mining Area
by Xianyao Jiang, Yi Xie, Chongning Na, Wenyang Yu and Yu Meng
Sustainability 2024, 16(7), 2827; https://doi.org/10.3390/su16072827 - 28 Mar 2024
Viewed by 724
Abstract
With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) [...] Read more.
With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) to contain dust points, leading to a significant decline in its detection performance, which makes it easy for vehicles to have failures at the perceptual level. In order to solve this problem, this study proposes a LiDAR point cloud denoising method for the quantitative analysis of laser reflection intensity and spatial structure. This method uses laser reflectivity as the benchmark template, constructs the initial confidence level template and initially screens out the sparse dust point cloud. The results are analyzed through the Euclidean distance of adjacent points, and the confidence level in the corresponding template is reduced for rescreening. The experimental results show that our method can significantly filter dust point cloud particles while retaining the rich environmental information of data. The computational load caused by filtering is far lower than that of other methods, and the overall operation efficiency of the system has no significant delay. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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17 pages, 14999 KiB  
Article
Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning
by Pingan Peng, Ru Lei and Jinmiao Wang
Sustainability 2023, 15(20), 14959; https://doi.org/10.3390/su152014959 - 17 Oct 2023
Cited by 1 | Viewed by 852
Abstract
As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal [...] Read more.
As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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16 pages, 1219 KiB  
Article
Research on Optimization of Monitoring Nodes Based on the Entropy Weight Method for Underground Mining Ventilation
by Shouguo Yang, Xiaofei Zhang, Jun Liang and Ning Xu
Sustainability 2023, 15(20), 14749; https://doi.org/10.3390/su152014749 - 11 Oct 2023
Cited by 1 | Viewed by 701
Abstract
Air pressure monitoring is the basis of mining-intelligent ventilation. In order to optimize the coverage of monitoring nodes, the node importance in the ventilation network was taken as the optimization basis in this study. Two evaluation indexes of the extent of node coverage [...] Read more.
Air pressure monitoring is the basis of mining-intelligent ventilation. In order to optimize the coverage of monitoring nodes, the node importance in the ventilation network was taken as the optimization basis in this study. Two evaluation indexes of the extent of node coverage and the influence degree of nodes were obtained by analyzing the influence degree of node air pressure. The entropy weight method (EWM) was used to weigh the evaluation indexes to obtain the importance of all nodes in the ventilation network. A node layout method with node importance as the optimization of air pressure-monitoring nodes was proposed. The minimum distance correlation between the limited monitoring nodes and the monitored nodes was set as the constraint condition, and any air pressure monitoring node could only monitor its adjacent nodes. The nodes with high node importance were selected as air pressure-monitoring nodes in turn until the coverage of air pressure-monitoring nodes in the ventilation network was maximized. By applying the entropy weight method (EWM) and the clustering algorithm (CA) to the case mine, the research results show that the application of the entropy weight method (EWM) to optimize the air pressure-monitoring nodes was more feasible than the clustering algorithm (CA). The coverage rate was 81.6% at different constraint values, and the maximum coverage rate was 92.1%, which meets the needs of arranging the least air pressure-monitoring nodes to monitor the maximum range of air pressure changes and can carry out full coverage monitoring of mine air pressure. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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13 pages, 5525 KiB  
Article
Study on the Influence of Wet Backfilling in Open Pit on Slope Stability
by Qiusong Chen, Yufeng Niu and Chongchun Xiao
Sustainability 2023, 15(16), 12492; https://doi.org/10.3390/su151612492 - 17 Aug 2023
Cited by 2 | Viewed by 871
Abstract
The residual open pit left in the wake of open-pit mining poses significant safety hazards, with backfilling being an effective strategy to wholly eliminate these risks. The stability of the slope following wet backfilling, however, should not be overlooked. This paper examines the [...] Read more.
The residual open pit left in the wake of open-pit mining poses significant safety hazards, with backfilling being an effective strategy to wholly eliminate these risks. The stability of the slope following wet backfilling, however, should not be overlooked. This paper examines the impact of the seepage field conditions and backfill height on the stability of open-pit slopes using a case study of cemented backfill in a specific open pit in Anhui Province. Moreover, it utilizes onsite research, Slide simulations, and similar simulation tests. The study findings suggest that as the height of the tailing solidification backfill increases, the safety factor of open-pit slopes gradually elevates. When the backfill height exceeds 10 m, all profiles of the studied open-air slope fulfill the stability prerequisites. Furthermore, when the solidification backfill reaches 20 m, all profiles of the studied open-pit slope satisfy the stability requirements. The research outcomes offer a methodology for mining corporations to avert slope instability and destruction, thereby providing effective safeguards for the extraction of scarce resources in mines. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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14 pages, 1839 KiB  
Article
Optimization of Branch Airflow Volume for Mine Ventilation Network Based on Sensitivity Matrix
by Jie Hou, Gang Nie, Guoqing Li, Wei Zhao and Baoli Sheng
Sustainability 2023, 15(16), 12427; https://doi.org/10.3390/su151612427 - 16 Aug 2023
Viewed by 966
Abstract
Underground mines have gradually entered the stage of deep mining with the consumption of shallow mineral resources, which makes mine ventilation networks generally complicated and the problem of unstable supply of branch airflow volume in deep-level ventilation networks increasingly serious. The scientific distribution [...] Read more.
Underground mines have gradually entered the stage of deep mining with the consumption of shallow mineral resources, which makes mine ventilation networks generally complicated and the problem of unstable supply of branch airflow volume in deep-level ventilation networks increasingly serious. The scientific distribution of the airflow volume between operation areas has become an important problem in the optimization of mine ventilation systems. This study takes the ventilation system of the Xinli Submine of Sanshandao Gold Mine as an example to analyze the airflow volume regulation demand of the deep-level section stope to further improve the coordination of the airflow volume distribution in the underground mine. The drawing and equivalent simplification of the ventilation network diagram are completed according to the engineering parameters of the target level roadway, and the sensitivity matrix is calculated using a formula. The optimization of the adjustment branch and the formulation of the adjustment scheme are carried out based on the sensitivity matrix. By realizing the adjustment objective of the branch airflow volume via comparing the airflow volume of the ventilation network before and after adjustment, the adjustment scheme can make the airflow volume distribution in the level more balanced. The results of our study show that branch sensitivity theory is theoretically feasible for analyzing and solving the problem of the mine ventilation network, which has certain practical significance for the adjustment of airflow volume in mines. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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21 pages, 6228 KiB  
Article
Research on the Physical and Chemical Characteristics of Dust in Open Pit Coal Mine Crushing Stations and Closed Dust Reduction Methods
by Zhichao Liu, Zhongchen Ao, Wei Zhou, Baowei Zhang, Jingfu Niu, Zhiming Wang, Lijie Liu, Zexuan Yang, Kun Xu, Wenqi Lu and Lixia Zhu
Sustainability 2023, 15(16), 12202; https://doi.org/10.3390/su151612202 - 9 Aug 2023
Cited by 1 | Viewed by 1368
Abstract
As an important link in open-pit mining production, the crushing station produces a large amount of dust during the production process. Dust has the characteristics of a wide spread area, great harm, and difficult governance. Therefore, dust control has become a key issue [...] Read more.
As an important link in open-pit mining production, the crushing station produces a large amount of dust during the production process. Dust has the characteristics of a wide spread area, great harm, and difficult governance. Therefore, dust control has become a key issue that needs to be solved in open-pit mining. In this article, we assess results after high-speed cameras and dust concentration detectors are installed around the crushing station to monitor the dust concentration in the surrounding air. It is found that in the air, dust with a particle size of less than 2.5 μm accounts for 67.43%, less than 10 μm accounts for 17.30%, and less than 100 μm accounts for 15.27%. In settled dust on the ground, particles with a particle size of less than 100 μm account for 42.69% of the sample, and particles less than 10 μm account for 16.60% of the sample. Secondly, physical and chemical properties testing is conducted on the dust. XRD test results show that SiO2 in the dust accounts for 65.80%; XRF test results show that the oxide Al2O3 in the dust accounts for up to 46.84%; ICP test results show that the element Al accounts for 42.62% of the total amount of trace elements detected; and Si accounts for 35.11%, clarifying the content of harmful substances to the human body. Finally, Fluent software, Ansys 2020 R1, is used to simulate the diffusion law of dust under different states of the crushing station, including an open state, a closed state, and the installation of a dust removal system. Based on the simulation results and the actual situation on site, the optimal dust reduction method suitable for the crushing station is proposed, and the diffusion law of dust under this method is simulated. The tracked dust shows that the dust removal efficiency of PM2.5 reaches 97.00%, PM10 reaches 99.60%, and TSP reaches 98.30%. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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21 pages, 8613 KiB  
Article
CFD Simulation Based Ventilation and Dust Reduction Strategy for Large Scale Enclosed Spaces in Open Pit Coal Mines—A Case of Coal Shed
by Zhongchen Ao, Zhiming Wang, Wei Zhou, Yanzhen Qiao, Abdoul Wahab, Zexuan Yang, Shouhu Nie, Zhichao Liu and Lixia Zhu
Sustainability 2023, 15(15), 11651; https://doi.org/10.3390/su151511651 - 28 Jul 2023
Cited by 2 | Viewed by 1227
Abstract
The coal shed is an enclosed space where raw coal is stored and handled. The intensive operation of the machinery inside the coal shed generates a large amount of dust, and the wind speed inside the enclosed space easily leads to a high [...] Read more.
The coal shed is an enclosed space where raw coal is stored and handled. The intensive operation of the machinery inside the coal shed generates a large amount of dust, and the wind speed inside the enclosed space easily leads to a high concentration of dust, which endangers the physical and mental health of the workers. In this paper, we first studied the particle size distribution of dust samples in the coal shed and found that 12.2% of the dust in the air of the coal shed was 10–100 μm, 87.8% was less than 10 μm, and 72.9% was less than 2.5 μm. Fluent was used to simulate the law of dust dispersion in the coal shed under different working conditions, and finally, the simulation results were used to guide the design of the ventilation site and dust-reduction scenario. The experimental and simulation results show that under the same working conditions, the average dust reduction efficiency of the ventilation method in which the north side and south side pump air outside was 9.9%. The ventilation method in which the north side blows inside and the south side pumps outside was 23.7%. The average dust reduction efficiency of the ventilation method in which the north side blows inside and the south side pumps outside + placing the fan in the middle was 59.9%. The research results can provide some reference value for indoor air quality improvement. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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15 pages, 4163 KiB  
Article
Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data
by Xingbang Qiang, Guoqing Li, Jie Hou, Xia Zhang and Yujia Liu
Sustainability 2023, 15(13), 10086; https://doi.org/10.3390/su151310086 - 26 Jun 2023
Viewed by 1238
Abstract
In view of the new situation faced by safety risk management in underground metal mines, based on a comprehensive analysis of the current situation of mine safety management business and system construction requirements, the main functional modules, overall architecture, and data interaction mode [...] Read more.
In view of the new situation faced by safety risk management in underground metal mines, based on a comprehensive analysis of the current situation of mine safety management business and system construction requirements, the main functional modules, overall architecture, and data interaction mode of the intelligent safety risk analysis and decision-making system were analyzed and designed. On the basis of elaborating the implementation process of the main functional modules of the system, such as multi-source safety information collection and governance, and safety risk intelligence analysis and visualization, a safety risk intelligence analysis and decision-making system was constructed, which provided efficient and real-time intelligent application and analysis services for safety in the production of underground metal mines and realized the whole process information management of collection, aggregation, processing, analysis, and visual display of multi-source mine safety risk information. The application of the system has made an essential change in the mode of mine safety risk management, realizing the active safety management goal of shifting safety risk management from post-analysis to pre-prevention, helping to improve the pertinence and efficiency of safety risk management, and greatly reducing the risk of mine safety accidents. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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19 pages, 9938 KiB  
Article
Drilling Path Planning of Rock-Drilling Jumbo Using a Vehicle-Mounted 3D Scanner
by Yongfeng Li, Pingan Peng, Huan Li, Jinghua Xie, Liangbin Liu and Jing Xiao
Sustainability 2023, 15(12), 9737; https://doi.org/10.3390/su15129737 - 19 Jun 2023
Cited by 3 | Viewed by 1579
Abstract
Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects [...] Read more.
Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects present a significant challenge for automated drilling under non-ideal surfaces, which constrains the intelligentization of rock excavation. To address this issue, this paper proposes a method for extracting contour surfaces and planning drilling paths based on a vehicle-mounted 3D scanner. This method effectively extracts contour surfaces and optimizes drilling paths, thereby improving work efficiency and safety. Specifically, the proposed method includes: (i) the real-time scanning of cross-sectional contours using a vehicle-mounted 3D scanner to construct an accurate three-dimensional point-cloud model and obtain contour over-digging information; the acquired data are compared with theoretical drilling maps in the vehicle’s coordinate system to re-plan the blasting-hole point set; (ii) the development of a volume-based dynamic search algorithm based on the irregularities of contour surfaces to detect potential collisions between holes; and (iii) the conversion of the drilling sequence planning based on the new blasting hole point set into a traveling salesman problem (TSP), and optimization using a Hybrid Greedy Genetic Algorithm (HGGA) to achieve path traversal of all drilling positions. The effectiveness of the proposed method was verified using rock excavation in a certain mine as an example. The results show that the overall recognition rate of the contour over-digging reached over 80%, the number of arm collisions was significantly reduced, and the distance traveled by the drilling rig was reduced by 35% using the improved genetic algorithm-based rock-drilling rig path planning. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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17 pages, 8205 KiB  
Article
A Lean Scheduling Framework for Underground Mines Based on Short Interval Control
by Hao Wang, Xiaoxia Zhang, Hui Yuan, Zhiguang Wu and Ming Zhou
Sustainability 2023, 15(12), 9195; https://doi.org/10.3390/su15129195 - 7 Jun 2023
Viewed by 1547
Abstract
Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool [...] Read more.
Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool to adapt intelligent scheduling in underground mines. The framework shortens the production monitoring and adjustment cycle to near-real-time, enabling timely corrective measures to minimize schedule deviations and improve overall production efficiency. An intelligent scheduling platform is implemented by adopting the digital twin platform framework, the intelligent scheduling mobile terminal module, and the integrated scheduling control cockpit module. The results indicate that the platform is effective in promoting mine intelligence by providing benefits in information transparency, flexible scheduling, lean production, and scientific decision-making. The proposed framework provides a practical solution for implementing intelligent scheduling in underground mines, contributing to the overall improvement of mine productivity. Overall, this paper provides insights for implementing intelligent scheduling in underground mines. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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18 pages, 4380 KiB  
Article
A Dynamic Scheduling Model for Underground Metal Mines under Equipment Failure Conditions
by Siyu Tu, Mingtao Jia, Liguan Wang, Shuzhao Feng and Shuang Huang
Sustainability 2023, 15(9), 7306; https://doi.org/10.3390/su15097306 - 27 Apr 2023
Viewed by 1320
Abstract
Equipment failure is a common problem in mining operations, resulting in significant delays and reductions in production efficiency. To address this problem, this paper proposes a dynamic scheduling model for underground metal mines under equipment failure conditions. The model aims to minimize the [...] Read more.
Equipment failure is a common problem in mining operations, resulting in significant delays and reductions in production efficiency. To address this problem, this paper proposes a dynamic scheduling model for underground metal mines under equipment failure conditions. The model aims to minimize the impact of equipment failures on production operations while avoiding extensive equipment changes. A case study of the southeastern mining area of the Chambishi Copper Mine is presented to demonstrate the effectiveness of the proposed model. The initial plan was generated using the multi-equipment task assignment model for the horizontal stripe pre-cut mining method. After equipment breakdown, the proposed model was used to reschedule the initial plan. Then, a comparative analysis was carried out. The results show that the proposed model effectively reduces the impact of equipment failures on production operations and improves overall mining execution at a low management cost. In general, the proposed model can assist schedulers in allocating equipment, coping with the disturbing effects of equipment failure, and improving mine production efficiency. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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16 pages, 3937 KiB  
Article
Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader
by Jicheng Wan, Xuhui Zhang, Chao Zhang, Wenjuan Yang, Mengyu Lei, Yuyang Du and Zheng Dong
Sustainability 2023, 15(5), 4018; https://doi.org/10.3390/su15054018 - 22 Feb 2023
Cited by 5 | Viewed by 1209
Abstract
Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the [...] Read more.
Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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19 pages, 9525 KiB  
Article
Stability Analysis of Multi-Layer Highwall Mining: A Sustainable Approach for Thick-Seam Open-Pit Mines
by Ya Tian, Lixiao Tu, Xiang Lu, Wei Zhou, Izhar Mithal Jiskani, Fuming Liu and Qingxiang Cai
Sustainability 2023, 15(4), 3603; https://doi.org/10.3390/su15043603 - 15 Feb 2023
Cited by 2 | Viewed by 1580
Abstract
Open-pit mining is a common method for extracting coal, but considerable resources are often left unrecovered at the bottom of end-slopes, leading to a permanent waste of resources. This research presents a sustainable approach of multi-layer highwall mining at different levels to address [...] Read more.
Open-pit mining is a common method for extracting coal, but considerable resources are often left unrecovered at the bottom of end-slopes, leading to a permanent waste of resources. This research presents a sustainable approach of multi-layer highwall mining at different levels to address the issue of abundant resources left unrecovered at the bottom of the end-slope in thick-seam open-pit mines. The interlayer between the upper and lower entries is simplified into a beam structure model, the bending moment distribution characteristics of the beam under a load of highwall miner are analyzed, and a method for calculating the thickness range of the interlayer is proposed. The web pillar width and interlayer thickness, obtained theoretically, are verified through a numerical simulation, and the results of mining a single layer are compared to those of mining multiple layers. The results show that the web pillar width and interlayer thickness derived from the numerical simulation are basically the same as those of the theoretical analysis. Compared with single layer mining, the vertical stress on the web pillar in the lowest panel is reduced by 14.83~18.25%, and the safety factor of the web pillar is increased to 0.27. The web pillars and interlayers at different elevations are stable during multi-layer highwall mining. These findings support the feasibility of multi-layer highwall mining for resource recovery, which is conducive to sustainable mining. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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12 pages, 1762 KiB  
Article
Open-Pit Mine Truck Dispatching System Based on Dynamic Ore Blending Decisions
by Jiang Yao, Zhiqiang Wang, Hongbin Chen, Weigang Hou, Xiaomiao Zhang, Xu Li and Weixing Yuan
Sustainability 2023, 15(4), 3399; https://doi.org/10.3390/su15043399 - 13 Feb 2023
Cited by 3 | Viewed by 2477
Abstract
In the production process of open-pit mines, trucks are applied in the production process of open-pit mines for transporting ores and rocks. Most open-pit mines are equipped with dozens of trucks. It is important to plan the dispatch of trucks in the production [...] Read more.
In the production process of open-pit mines, trucks are applied in the production process of open-pit mines for transporting ores and rocks. Most open-pit mines are equipped with dozens of trucks. It is important to plan the dispatch of trucks in the production process so that the transportation process can be the shortest in distance, the lowest in cost, and the most efficient. At present, many open-pit mining enterprises have realized the use of dispatching systems to schedule trucks to complete production tasks. However, these methods are mostly designed to deploy trucks to reduce production costs without considering the blending problem of the selected ores, and therefore it cannot meet the dual need of ore blending and dispatching. In order to solve the above technical problems and meet the actual needs of the current open-pit mine for ore blending and dispatching, this paper proposes an open-pit mine truck dispatching system based on dynamic ore blending decisions, supported by a 4G/5G wireless network, Beidou positioning, and Internet of Things technology, which can not only realize the optimized truck dispatching of open-pit mine production, but also meet the requirements of downstream concentrators for ore dressing grade. The system has been applied in the Ansteel Group QIDASHAN mine for one year. The proportion of trucks dispatched through the system reached more than 70%. The trucks’ capacity were upgraded from 3.79 to 4 million ton km per set per year, and the efficiency was improved by 5.5%. The limitations of the proposed system and method mainly include the possibility of inaccurate measurement of ore output and the lack of combination with unmanned driving. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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17 pages, 3530 KiB  
Article
Production Strategy Optimization of Integrated Exploitation for Multiple Deposits Considering Carbon Quota
by Yingyu Gu, Guoqing Li, Jie Hou, Chunchao Fan, Xingbang Qiang, Bin Bai and Yongfang Zhang
Sustainability 2023, 15(4), 2917; https://doi.org/10.3390/su15042917 - 6 Feb 2023
Viewed by 1422
Abstract
Nowadays, the mining industry actively advocates and practices the concept of green and integrated exploitation to realize the sustainable development of resources with low-carbon emissions. The certain carbon quota for mining companies limits the production capacity and resource utilization efficiency. The integrated exploitation [...] Read more.
Nowadays, the mining industry actively advocates and practices the concept of green and integrated exploitation to realize the sustainable development of resources with low-carbon emissions. The certain carbon quota for mining companies limits the production capacity and resource utilization efficiency. The integrated exploitation of multiple deposits could coordinate resource allocation and operation facilities, which would reduce capital expenditure and operating costs for the mining company from a systematic perspective. In this condition, some deposits located nearby could be treated as one entity to make plans and optimize. An optimization framework is proposed based on the analysis of the characteristics and advantages of integrated exploitation. A new mathematical programming model is presented to optimize production capacity and extracted ore grade for each deposit considering constraints of maximum and minimum mining capacity, extracted ore grade and concentrated ore grade requirement, and metal output target, which has a significant influence on the economic benefit and resource utilization rate for a mining company. The model is verified using the data collected from three deposits of a gold mining company in China to demonstrate its ability to optimize the allocation of production capacity and improve the technical and economic effect of mining under the limitation of carbon quota. The sensitivity analysis of some key parameters is carried out to generate a series of integrated exploitation schemes under different production and operation conditions, which is useful for the mining company to make decisions in different situations. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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18 pages, 2595 KiB  
Article
Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms
by Yukun Yang, Wei Zhou, Izhar Mithal Jiskani, Xiang Lu, Zhiming Wang and Boyu Luan
Sustainability 2023, 15(2), 1169; https://doi.org/10.3390/su15021169 - 8 Jan 2023
Cited by 12 | Viewed by 2709
Abstract
Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as [...] Read more.
Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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15 pages, 7742 KiB  
Article
Gradient-Based Automatic Exposure Control for Digital Image Correlation
by Jiangping Chen and Weijun Tao
Sustainability 2023, 15(2), 1149; https://doi.org/10.3390/su15021149 - 7 Jan 2023
Viewed by 1055
Abstract
Digital image correlation (DIC) is widely used in material experiments such as ores; the quality of a speckle image directly affects the accuracy of the DIC calculation. This study aims to acquire high-quality speckle pattern images and improve the calculation accuracy and stability. [...] Read more.
Digital image correlation (DIC) is widely used in material experiments such as ores; the quality of a speckle image directly affects the accuracy of the DIC calculation. This study aims to acquire high-quality speckle pattern images and improve the calculation accuracy and stability. A gradient-based image quality metric was selected to evaluate the image quality, and its validity was verified by a rigid body experiment and a numerical experiment. Based on the maximum image quality metric, an automatic exposure control algorithm and the control procedure were proposed to obtain the optimal exposure time. Finally, nine sets of images with different poses and illuminations were captured, and displacement and strain fields were calculated at the fixed exposure time and the optimized exposure time. The results of the rigid-body motion experiment show that the calculated data at the optimized exposure time is smoother and less noisy, and the error is smaller, which verifies the effectiveness of the exposure control procedure and its algorithm and improves the accuracy and stability of DIC calculation. Full article
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14 pages, 2688 KiB  
Article
Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines
by Jie Hou, Guoqing Li, Lianyun Chen, Hao Wang and Nailian Hu
Sustainability 2023, 15(1), 216; https://doi.org/10.3390/su15010216 - 23 Dec 2022
Cited by 4 | Viewed by 2158
Abstract
The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using [...] Read more.
The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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16 pages, 6306 KiB  
Article
Determination of the Required Strength of Artificial Roof for the Underhand Cut-and-Fill Mine Using Field Measurements and Theoretical Analysis
by Bin Han, Kun Ji, Jiandong Wang, Shibo Wang, Peng Zhang and Yafei Hu
Sustainability 2023, 15(1), 189; https://doi.org/10.3390/su15010189 - 22 Dec 2022
Cited by 2 | Viewed by 1365
Abstract
For the underhand cut-and-fill mining method, to ensure safe and economic mining, a key issue is to correctly determine the required strength of the artificial roof made of cemented paste backfill (CPB). However, the determination of the required strength is typically based on [...] Read more.
For the underhand cut-and-fill mining method, to ensure safe and economic mining, a key issue is to correctly determine the required strength of the artificial roof made of cemented paste backfill (CPB). However, the determination of the required strength is typically based on historical experience and analytical beam formulas, resulting in the obtained required strength being unsuitable for the actual situation. Therefore, in order to determine the required strength of the CPB roof reasonably and accurately, field measurements based on sensors were proposed and carried out in the Jinchuan mine, and then formulas based on thick plate theory were derived to verify the measured results. The results show that the required strength obtained by field measurement is 0.325 MPa and that obtained by thick plate theory is 0.304 MPa, with an error of 6.78% between them, verifying the accuracy of the measurements. However, the strength standard currently used by Jinchuan is 0.59 MPa, which far exceeds the optimal strength and results in many additional, unnecessary expenses. To ensure economical mining, the span of the drift was enlarged from 5.0 m to 6.0 m based on the results of the actual measurements and the current production status of the mine. The measurements show that the maximum cumulative subsidence of the drift roof is 11.69 mm and the maximum convergence deformation of the sidewalls is 8.34 mm, which indicates that the stability of the span-enlarged drift is satisfactory. Meanwhile, enlarging the drift span allows for a 20% increase in production capacity per mining cycle. This field measurement method and theoretical analysis model can be used as an efficient guide to facilitate the design of underhand cut-and-fill mining. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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18 pages, 2743 KiB  
Article
A Multi-Equipment Task Assignment Model for the Horizontal Stripe Pre-Cut Mining Method
by Siyu Tu, Mingtao Jia, Liguan Wang, Shuzhao Feng and Shuang Huang
Sustainability 2022, 14(24), 16379; https://doi.org/10.3390/su142416379 - 7 Dec 2022
Cited by 2 | Viewed by 1120
Abstract
This paper proposes a multi-equipment task assignment model for the horizontal stripe pre-cut mining method to address the problem of cooperative scheduling operation of multi-equipment in underground metal mines under complex constraints. The model is constructed with multiple objectives, including operation time, operational [...] Read more.
This paper proposes a multi-equipment task assignment model for the horizontal stripe pre-cut mining method to address the problem of cooperative scheduling operation of multi-equipment in underground metal mines under complex constraints. The model is constructed with multiple objectives, including operation time, operational efficiency, equipment utilization rate, and ore grade fluctuation by considering the constraints of time, space, equipment, and processes. The NSGA-III algorithm is used to obtain the solution. The effectiveness of the algorithm is tested based on the actual data from the Chambishi Copper Mine. The results show that the average equipment utilization rate is 51.25%, and the average ore output efficiency is 278.71 tons/hour. The NSGA-III algorithm can quickly generate the optimal multi-equipment task assignment solution. The solution reduces the interference of manual experience and theoretically improves the actual operation of the mine. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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17 pages, 4478 KiB  
Article
Application of Extended Set Pair Analysis on Wear Risk Evaluation of Backfill Pipeline
by Zaihai Wu, Zhaojun Qi, Yunpeng Kou, Zheng Li, Guoyan Zhao and Weizhang Liang
Sustainability 2022, 14(23), 15535; https://doi.org/10.3390/su142315535 - 22 Nov 2022
Cited by 1 | Viewed by 959
Abstract
Filling slurry can inevitably cause irreversible wear to the pipeline, which represents great costs to mines. This study aims to propose an extended set pair analysis (SPA) for the wear risk evaluation of backfill pipeline. First, to fully describe the wear risk of [...] Read more.
Filling slurry can inevitably cause irreversible wear to the pipeline, which represents great costs to mines. This study aims to propose an extended set pair analysis (SPA) for the wear risk evaluation of backfill pipeline. First, to fully describe the wear risk of backfill pipeline, an evaluation index system was established from the aspects of slurry characteristics, pipeline properties, and slurry flow state. Then, the experts grading method was modified with probabilistic linguistic term sets (PLTSs) to obtain subjective weights. Meanwhile, the criteria importance through intercriteria correlation (CRITIC) approach was used to calculate objective weights. By introducing a preference coefficient, they were integrated to determine the comprehensive weights. After that, the classical SPA was extended with membership functions and fuzzy entropy theory, so that the wear risk of backfill pipeline can be evaluated from the perspectives of both the risk level and complexity. Finally, the proposed methodology was applied to assess the wear risk in the Jinchuan nickel mine, Dahongshan copper mine, Hedong gold mine, and Xincheng gold mine. The reliability of evaluation results was further verified through sensitivity and comparative analyses. Results indicate that the proposed methodology is feasible for the wear risk evaluation of backfill pipeline, and can provide guidance on the wear risk management. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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18 pages, 6902 KiB  
Article
Modeling and Simulation of Unmanned Driving System for Load Haul Dump Vehicles in Underground Mines
by Yuanjian Jiang, Pingan Peng, Liguan Wang, Jiaheng Wang, Yongchun Liu and Jiaxi Wu
Sustainability 2022, 14(22), 15186; https://doi.org/10.3390/su142215186 - 16 Nov 2022
Cited by 2 | Viewed by 1530
Abstract
This paper proposes the modeling and simulation of the unmanned driving system for underground load haul dump vehicles based on Gazebo/Ros. Firstly, the kinematics model of the load haul dump vehicle is derived. Then, the model of each part of the load haul [...] Read more.
This paper proposes the modeling and simulation of the unmanned driving system for underground load haul dump vehicles based on Gazebo/Ros. Firstly, the kinematics model of the load haul dump vehicle is derived. Then, the model of each part of the load haul dump vehicle is established based on SolidWorks and the model of the load haul dump vehicle is established by connecting the parts through a unified robot description format (URDF) file. Finally, the laneway model is established by using alpha shape to realize the modeling of the operating environment of the load haul dump vehicle. The speed, angular speed, bucket lifting, and bucket flipping of the load haul dump vehicle are controlled using PID. The experimental results show that: The control errors of the speed and angular speed of the load haul dump vehicle are 0.283 m/s and 0.010 rad/s, respectively. The control error of the lifting bucket is 0.025 m and that of the flipping bucket is 0.015 m. The angular velocity control error of the simulation system relative to the actual system is 0.330 and 0.106 m/s, respectively. The error between the SLAM of the simulation system and the actual system and the measured value is 0.917 and 3.44 m, respectively. The control performance of the load haul dump vehicle in the simulation system is good. Therefore, automatic driving algorithms can be studied and tested in this simulation platform. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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16 pages, 3995 KiB  
Article
Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm
by Jiang Yao, Zhiqiang Wang, Chunhui Liu, Guichen Huang, Qingbo Yuan, Kai Xu and Wenhui Zhang
Sustainability 2022, 14(21), 14386; https://doi.org/10.3390/su142114386 - 3 Nov 2022
Cited by 4 | Viewed by 1299
Abstract
With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the [...] Read more.
With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the crushing mouth. Based on deep learning (DL) and machine vision (MV) technology, an on-line detection method is studied to trace the blockage in a swift and accurate manner, so that the corresponding detection system can be designed accordingly. The charge coupled device (CCD) industrial camera set above the crushing mouth is used to collect images and input them to the edge computing equipment. The original Single Shot MultiBox Detector (SSD) preprocessing model is trained and optimized before it is combined with the MV technology to detect and then the MV technology is combined to detect whether the crushing mouth is covered. In Ansteel Group GUANBAOSHAN mine, the accuracy of recognition and detection system with human observation was examined for one month, and the tested accuracy is 95%. The experimental results show that the proposed method can detect the crushing mouth blockage in real time, which would solve the problem that the blockage can only be identified by human eyes in traditional method, and provides basic support for the unattended crushing station. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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12 pages, 4406 KiB  
Article
Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model
by Zhongyuan Gu, Miaocong Cao, Chunguang Wang, Na Yu and Hongyu Qing
Sustainability 2022, 14(16), 10421; https://doi.org/10.3390/su141610421 - 22 Aug 2022
Cited by 15 | Viewed by 2084
Abstract
The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and [...] Read more.
The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model prediction evaluation, the results show that the R2 (coefficient of determination) of the prediction results of the GA-XGBoost model is 0.941, the RMSE (root mean square error) is 0.369, and the MAE (mean absolute error) is 0.308. Then, compared with classic ensemble learning models such as XGBoost, random deep forest, and gradient boost, the GA-XGBoost model has higher prediction accuracy and performance than a single machine learning model. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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16 pages, 835 KiB  
Article
Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine
by Xiangyu Kou, Xuebin Xie, Yi Zou, Qian Kang and Qi Liu
Sustainability 2022, 14(15), 9062; https://doi.org/10.3390/su14159062 - 24 Jul 2022
Cited by 4 | Viewed by 1520
Abstract
In this paper, a comprehensive evaluation factor model of a truck dispatching system in open-pit mines is constructed from the three dimensions of optimal route, traffic flow planning, and real-time dispatching, and the final combined weight of the factor is determined according to [...] Read more.
In this paper, a comprehensive evaluation factor model of a truck dispatching system in open-pit mines is constructed from the three dimensions of optimal route, traffic flow planning, and real-time dispatching, and the final combined weight of the factor is determined according to game theory. On this basis, a comprehensive evaluation model of a truck dispatching system in open-pit mines based on gray relational analysis—technology for order preference by similarity to an ideal solution (GRA-TOPSIS) is established. Taking the truck dispatching system in five open-pit mines as the research background, the advantages and disadvantages of the dispatching system were comprehensively evaluated, and the differences between the dispatching systems were analyzed using the radar chart method. The research shows that the evaluation results of the comprehensive evaluation model of the truck dispatching system in open-pit mines based on GRA-TOPSIS are in line with the reality, which is more conducive to analyzing and comparing the advantages and disadvantages of the systems, effectively identifying the differences of various systems, and making the evaluation of truck dispatching systems more scientific. The research results of this paper broaden the evaluation of truck dispatching systems and provide a theoretical basis for the optimization of truck dispatching systems. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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17 pages, 4215 KiB  
Article
Full Waveform Prediction of Blasting Vibration Using Deep Learning
by Yunsen Wang, Guiping Zheng, Yuanhui Li and Fengpeng Zhang
Sustainability 2022, 14(13), 8200; https://doi.org/10.3390/su14138200 - 5 Jul 2022
Cited by 6 | Viewed by 1874
Abstract
Blasting vibration could cause dynamic instability of rock masses within a critical steady state. To control the blasting vibration, it is necessary to understand the complete dynamic response process of the rock masses under the blasting vibration. The Long Short-Term Memory (LSTM) technique [...] Read more.
Blasting vibration could cause dynamic instability of rock masses within a critical steady state. To control the blasting vibration, it is necessary to understand the complete dynamic response process of the rock masses under the blasting vibration. The Long Short-Term Memory (LSTM) technique uses blast monitoring data to predict the full waveform of the blast vibration. Based on the LSTM, a new full waveform prediction model is proposed in this study. To verify the feasibility of the proposed model, the sample data were constructed using the well-known linear blast wave superposition prediction formula. The full waveform prediction model is trained and the predicted waveform and the actual waveform are then evaluated and compared. The loss function is calculated and discussed, which verifies the feasibility of the prediction method. In addition to the numerical research, the actual blasting vibration data are also used for verification. The parameters, such as sequence size, training algorithm, and some hidden layer nodes, are discussed and optimized. The results show that the proposed full waveform prediction model based on LSTM can predict the full blasting waveform. This study provides a new idea for the prediction and control of blasting vibration. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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12 pages, 9388 KiB  
Article
Risk Monitoring Level of Stope Slopes and Landslides in High-Altitude and Cold Mines
by Ruichong Zhang, Shiwei Wu, Chengyu Xie and Qingfa Chen
Sustainability 2022, 14(13), 7581; https://doi.org/10.3390/su14137581 - 22 Jun 2022
Cited by 4 | Viewed by 1391
Abstract
To study the landslide risk of high-altitude and cold stope slopes, the slope deformation index and landslide risk standards at home and abroad for many years were analyzed and summarized. Using the unascertained measurement model, combined with the analytic hierarchy process, using the [...] Read more.
To study the landslide risk of high-altitude and cold stope slopes, the slope deformation index and landslide risk standards at home and abroad for many years were analyzed and summarized. Using the unascertained measurement model, combined with the analytic hierarchy process, using the Dongbang slope of the Beizhan Iron Mine in Hejing County, Xinjiang, as the research object, the detailed geological data of the slope were obtained, and nine factors affecting the landslide risk of the slope were analyzed. When calculating the weight of each factor, the actual situation of the slope was used as the standard, and the weight of each factor was determined by the analytic hierarchy process. Then, the undetermined measurement matrix of the slope was determined by the statistical method combined with the expert scoring results. Finally, an unconfirmed measurement model for landslide risk grade evaluation of the Dongbang slope of the Beizhan Iron Mine was formed, and the landslide risk monitoring grade evaluation was carried out on the slope in the cold area. The results show that the landslide risk monitoring level of the Dongbang slope in Beizhan Iron Mine is grade II, which indicates that there is a possibility of mild landslide risk for the slope in this cold area. The research results can provide a reference for the risk level and risk assessment of high-altitude alpine slopes. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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Review

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24 pages, 8643 KiB  
Review
In-Pit Disposal of Mine Tailings for a Sustainable Mine Closure: A Responsible Alternative to Develop Long-Term Green Mining Solutions
by Carlos Cacciuttolo and Edison Atencio
Sustainability 2023, 15(8), 6481; https://doi.org/10.3390/su15086481 - 11 Apr 2023
Cited by 5 | Viewed by 4931
Abstract
In the next decades many of the old tailings storage facilities (TSFs) could be re-processed if one considers the prices of metals, new uses of metals which today are not valuable, and the application of new, more efficient metallurgical technologies. In this context, [...] Read more.
In the next decades many of the old tailings storage facilities (TSFs) could be re-processed if one considers the prices of metals, new uses of metals which today are not valuable, and the application of new, more efficient metallurgical technologies. In this context, in-pit disposal of mine tailings (IPDMT) is an attractive alternative to be used as part of responsible mine closure: mines could reprocess the mine tailings and place them in an open pit as part of sustainable mine closure. This article explores a little-explored tailings disposal technique that has the potential to be considered as an environmentally friendly solution, returning mine tailings to their place of origin and providing long-term stability under a climate change scenario. This article presents the main features, benefits, and potential drawbacks of IPDMT, with an emphasis on: (i) a description of the main advantages and disadvantages of application; and design issues related to (ii) IPDMT physical stability (pit slope stability, tailings transport, placement systems); (iii) IPDMT hydrological stability (water management, seepage control, hydrogeological monitoring,); and (iv) IPDMT geochemical stability (geochemical characterization, acid rock drainage control, covers). The novelty of this article is the proposal to change the status quo of traditional management of mine tailings to a new paradigm where the technique of in-pit disposal of mine tailings can be considered a green mining solution for mine closure. Finally, some successful cases around the world that involved the implementation of this technique are presented. Full article
(This article belongs to the Special Issue Advances in Intelligent and Sustainable Mining)
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