Submit to Mathematics Review for Mathematics Propose a Special Issue

Journal Menu

Journal Browser

Mathematical Modeling and Analysis in Mining Engineering

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 19385

Share This Special Issue

Special Issue Editor

Dr. Lin Bi

E-Mail Website
Guest Editor

School of Resources and Safety Engineering, Central South University, Changsha, China
Interests: digital mine and intelligent mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of mining engineering, mathematics serves as a fundamental tool across various specialized domains, including (but not limited to) geological modeling and geostatistics, ventilation network analysis and solutions, mine production planning and scheduling, engineering geological model and stability analysis, and mining complex scene perception and modeling. Geological modeling and geostatistics leverage spatial interpolation methods like Kriging to estimate mineral deposit grades and variability, optimizing mine design and reducing uncertainty. Ventilation network analysis and solution employ graph theory and computational fluid dynamics to simulate airflow, ensuring worker safety and efficient contaminant dispersion. Mine production planning and scheduling utilize mathematical optimization techniques to schedule extraction activities, aligning with market demands and operational constraints. Engineering geological models incorporate stability analysis through numerical simulations and partial differential equations to assess rock mechanics and prevent failures. Mining complex scene perception and modeling integrate machine learning and computer vision algorithms to analyze sensor data, enhancing situational awareness and decision-making processes. These applications collectively demonstrate the critical role of mathematics in advancing the efficiency, safety, and sustainability of modern mining operations.

We seek papers that offer innovative approaches to geological modeling and geostatistics and that address the implementation of various interactions within mining systems. We also welcome research on topics such as ventilation network analysis and solutions, mine production planning and scheduling, engineering geological modeling and stability analysis, and mining complex scene perception and modeling. We are particularly interested in studies where the integration of mathematical and computational methods advances our understanding and solutions in these areas.

We look forward to receiving your contributions.

Dr. Lin Bi
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics 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 2600 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

geological modeling and geostatistics
ventilation network analysis and solutions
mine production planning and scheduling
engineering geological model and stability analysis
mining complex scene perception and modeling

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Mathematical Modeling and Analysis in Mining Engineering, 2nd Edition in Mathematics

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

18 pages, 4452 KB

Open AccessArticle

Fast 3D Gaussian Reconstruction for Open-Pit Mine Teleoperated Excavation via Monocular-LiDAR Fusion

by Lin Bi, Muqian Tan, Ziyu Zhao, Jinbo Li and Xintong Wang

Mathematics 2026, 14(7), 1191; https://doi.org/10.3390/math14071191 - 2 Apr 2026

Viewed by 329

Abstract

Teleoperated open-pit excavation requires fast and reliable 3D scene modeling under lightweight sensor configurations. To this end, this paper proposes a monocular camera–LiDAR fusion-based fast 3D Gaussian reconstruction method tailored for teleoperated open-pit excavation. The proposed approach uses only two sensors, a monocular camera and LiDAR, and integrates SPNet, a depth completion network, to improve the geometric completeness of the reconstructed scene. It further introduces a stride-aware initialization strategy that leverages the depth–stride correlation to jointly construct the initial Gaussian set and estimate the initial scales. During optimization, scale and color regularization are applied to prevent uncontrolled growth of Gaussians. Experiments in a Carla-simulated open-pit excavation scenario show that, under high-resolution input of 1920 × 1080, the proposed method achieves a stable 3D model update rate of approximately 2.5 Hz. The reconstruction quality under training viewpoints reaches PSNR 30.5388, SSIM 0.9161, and LPIPS 0.1333. Compared with 4DTAM and MonoGS, the proposed method achieves better overall reconstruction quality. It also maintains a much higher update rate than 4DTAM and a comparable update rate to MonoGS. Ablation studies further verify the critical contribution of the depth completion module and the stride-aware initialization strategy to the overall reconstruction performance. In addition, preliminary validation on field data further demonstrates the applicability of the proposed method under real-world open-pit excavation-loading conditions. The proposed method generates stable and usable 3D models of rock-pile working face under a lightweight sensor configuration, providing a reliable geometric basis for remote situational awareness and excavation assistance. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

42 pages, 7394 KB

Open AccessArticle

Statistical Modeling and Forecasting of Operational Reliability of Induction Motors of Mining Dump Trucks

by Aleksey F. Pryalukhin, Nikita V. Martyushev, Boris V. Malozyomov, Anton Y. Demin, Alexander V. Pogrebnoy, Elizaveta E. Kuleshova and Denis V. Valuev

Mathematics 2026, 14(4), 706; https://doi.org/10.3390/math14040706 - 17 Feb 2026

Cited by 2 | Viewed by 396

Abstract

This study presents a statistical modeling approach for predicting the operational reliability of induction motors used in dump truck drives. The proposed method uses censored data, including both time to failure and data on properly operating engines, to assess reliability indicators, such as uptime based on Weibull and lognormal distributions. A generalized “life curve” of the stator and bearing unit is constructed, which makes it possible to determine interval estimates of the service life and residual service life. The model is implemented as software for calculating distribution parameters and visualizing reliability dependencies. Approbation based on the operational data of quarry transport confirmed the applicability of the proposed approach for diagnosing and optimizing the maintenance system of induction motors of heavy equipment. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

20 pages, 9595 KB

Open AccessArticle

CCO–XGBoost Hybrid Model for Prediction of Blasting-Induced Peak Particle Velocity in Open-Pit Mines: A SHAP-Driven Sensitivity Analysis

by Chengye Yang, Jielin Li, Keping Zhou and Xin Xiong

Mathematics 2026, 14(4), 596; https://doi.org/10.3390/math14040596 - 9 Feb 2026

Viewed by 489

Abstract

Accurate prediction of peak particle velocity (PPV) in open-pit mine blasting is critical for ensuring operational safety and effective vibration control. This study proposes a hybrid modeling approach that integrates the Centered Collision Optimization (CCO) algorithm with Extreme Gradient Boosting (XGBoost), enhanced by SHAP-based sensitivity analysis to improve model transparency and mechanistic interpretability. A comprehensive dataset was constructed based on 193 field-measured blasting records collected from the Panzhihua Iron Mine in China, incorporating nine key input parameters. Model performance was rigorously evaluated using four widely recognized metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). The results demonstrate that the CCO–XGBoost model achieves superior predictive performance, with R² = 0.967, RMSE = 0.110, MAE = 0.067, and VAF = 96.35%, outperforming conventional approaches. SHAP-based sensitivity analysis reveals that blast-to-monitor distance (R) is the dominant negative predictor of PPV, contributing 43% to the total influence, with its vibration attenuation effect intensifying significantly when R exceeds 54 m. Charge per hole (q) and total charge per delay (Q) are identified as the primary positive influencing factors, accounting for 24% and 20% of the total contribution, respectively: the positive promoting effect of q on PPV strengthens markedly when q exceeds 17 kg, while Q exerts a continuous positive increasing influence on PPV when it exceeds 253 kg. Compared to existing hybrid models, the CCO–XGBoost uniquely avoids local optima and ensures higher global stability. This study fills the gap by providing quantifiable engineering thresholds for practical vibration control, making the model directly applicable to on-site blasting optimization. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

30 pages, 4195 KB

Open AccessArticle

Stability Analysis of Tunnel Face in Nonhomogeneous Soil with Upper Hard and Lower Soft Strata Under Unsaturated Transient Seepage

by Wenjun Shao, De Zhou, Long Xia, Guihua Long and Jian Wang

Mathematics 2026, 14(3), 537; https://doi.org/10.3390/math14030537 - 2 Feb 2026

Viewed by 351

Abstract

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (

C / D

), reaching a minimum critical support pressure at approximately

C / D = 1.1

. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

19 pages, 3623 KB

Open AccessArticle

Off-Site Geological Surveying of Longwall Face Based on the Fusion of Multi-Source Monitoring Data

by Mengbo Zhu, Ruoyu Rong, Zhizhen Liu, Xuebin Qin, Haonan Zhang and Shuaihong Kang

Mathematics 2025, 13(18), 3008; https://doi.org/10.3390/math13183008 - 17 Sep 2025

Cited by 1 | Viewed by 664

Abstract

A high-precision coal seam model is crucial to improving the adaptability of unmanned mining technology to geological conditions. However, the accuracy of a coal seam model constructed with boreholes and geophysical data is far from the required accuracy of unmanned mining (sub-decimeter level). Therefore, it is necessary to collect geological data revealed by mining and to update the coal seam model dynamically. As a solution to this problem, this paper proposes a new method for conducting off-site geological surveying of longwall faces by integrating multi-source monitoring data. The spatial attitudes of hydraulic supports are monitored to estimate the local dip angles of longwall face. A roof line calculation model was established, which integrates the local inclination angle of the longwall face, the number of hydraulic supports, and the roof elevation of the two roadways. Meanwhile, the local coal–rock columns at the camera observation point are extracted automatically using image segmentation and a proportional relationship between the picture and the actual scene. Coal and rock walls and a support guarding plate in the longwall face image are identified accurately using the coal-rock support segmentation model trained with U-net. Then, the height of the coal (or rock) wall above the coal–rock interface is estimated automatically according to the image segmentation and the similar proportion equation of actual longwall face and longwall face image. Combined with mining height information, the local coal–rock column can be extracted. Finally, the geological surveying profile of longwall face can be obtained by integrating the estimated roof line and local coal–rock columns. The field test demonstrated the efficacy of the method. This study helps to address a long-standing limitation of insufficient geological adaptability of intelligent mining technology. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

19 pages, 1130 KB

Open AccessArticle

Optimizing Mine Ventilation Systems: An Advanced Mixed-Integer Linear Programming Model

by Deyun Zhong, Lixue Wen, Yulong Liu, Zhaohao Wu and Liguan Wang

Mathematics 2025, 13(18), 2906; https://doi.org/10.3390/math13182906 - 9 Sep 2025

Cited by 2 | Viewed by 1316

Abstract

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced linear optimization technique. This method transforms the nonlinear ventilation optimization and regulation model into a linear control model, avoiding the limitation of difficulty in solving the nonlinear mathematical model. The linear strategy opens up a new solution idea for the nonlinear calculation of the mine ventilation optimization and regulation. Furthermore, this study introduces evaluation metrics for ventilation scheme quality, including minimal energy consumption, fewest adjustment points, and optimal placement of these points, enhancing flexibility in ventilation network optimization. By analyzing the ventilation model control objectives and constraints, we formulated a linear optimization model and developed a multi-objective mixed-integer programming model for ventilation network optimization. This paper constructs and verifies a calculation example model for mine ventilation optimization, assessing its reliability based on airflow distribution calculations. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

28 pages, 8337 KB

Open AccessArticle

Collision Detection Algorithms for Autonomous Loading Operations of LHD-Truck Systems in Unstructured Underground Mining Environments

by Mingyu Lei, Pingan Peng, Liguan Wang, Yongchun Liu, Ru Lei, Chaowei Zhang, Yongqing Zhang and Ya Liu

Mathematics 2025, 13(15), 2359; https://doi.org/10.3390/math13152359 - 23 Jul 2025

Cited by 1 | Viewed by 1638

Abstract

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

17 pages, 1050 KB

Open AccessArticle

Multi-Objective Ore Blending Optimization for Polymetallic Open-Pit Mines Based on Improved Matter-Element Extension Model and NSGA-II

by Jun Xiang, Jianhong Chen, Aishu Zhang, Xing Zhao, Shengyuan Zhuo and Shan Yang

Mathematics 2025, 13(11), 1843; https://doi.org/10.3390/math13111843 - 31 May 2025

Cited by 4 | Viewed by 1609

Abstract

With the increasing demand for mineral resources, sustainable mining development faces challenges such as low resource utilization efficiency. Ore blending optimization has emerged as a critical approach to enhance resource utilization. This study constructs a multi-objective ore blending optimization system for complex polymetallic open-pit mines based on the improved matter-element extension model and NSGA-II algorithm. By identifying key blending factors, objective functions are established to minimize both total ore quantity deviation and grade deviation, with six constraints defined to reflect production capacity limits. The NSGA-II algorithm is employed to solve the multi-objective optimization problem, generating a Pareto optimal solution set from which the optimal ore blending scheme is selected using the improved matter-element extension model. A case verification at Dabaoshan Mine demonstrates that the model-verified scheme achieves 1.035% higher total production accuracy than the planned value and 2.828% higher than actual production, while improving Cu grade deviation accuracy by 7.021% over the plan and 1.064% over actual production, and S grade deviation accuracy by 33.027% over the plan and 3.127% over actual production. This study, through the construction of systematic ore blending theory and empirical analysis, provides an important theoretical framework and methodological support for subsequent research on ore blending in polymetallic open-pit mines. It demonstrates significant practical application value in Dabaoshan Mine, offering an intelligent mine solution that combines scientific rationality and engineering practicability for polymetallic open-pit mines. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

19 pages, 16429 KB

Open AccessArticle

Three-Dimensional Stratigraphic Structure and Property Collaborative Modeling in Urban Engineering Construction

by Baoyi Zhang, Yanli Zhu, Tongyun Zhang, Xian Zhou, Binhai Wang, Or Aimon Brou Koffi Kablan and Jixian Huang

Mathematics 2025, 13(3), 345; https://doi.org/10.3390/math13030345 - 22 Jan 2025

Viewed by 1587

Abstract

In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by the stratigraphic structural model. A key innovation is the incorporation of a well-sampled geological stratigraphical potential field (SPF) as an ancillary variable, which enhances the interpolation of geological properties in areas with sparse and uneven sampling points. The proposed MRBF method outperforms traditional interpolation techniques by showing reduced dependency on the distribution of sampling points. Furthermore, the study calculates the bearing capacity of individual pile foundations based on precise stratigraphic thicknesses, yielding more accurate results compared to conventional methods that average these values across the entire site. Additionally, the integration of 3D geological models with urban planning facilitates the development of comprehensive urban digital twins, optimizing resource management, improving decision-making processes, and contributing to the realization of smart cities through more efficient data-driven urban management strategies. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

18 pages, 54250 KB

Open AccessArticle

Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning

by Shouye Cheng, Xin Yin, Feng Gao and Yucong Pan

Mathematics 2024, 12(23), 3832; https://doi.org/10.3390/math12233832 - 4 Dec 2024

Cited by 3 | Viewed by 1715

Abstract

Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a hybrid machine learning paradigm that integrates generative artificial intelligence and deep ensemble learning. Specifically, conditional tabular generative adversarial network is devised to solve the problems of data shortage and class imbalance for data augmentation at the data level, and the deep random forest is built based on the augmented data for subsequent squeezing classification. A total of 139 historical squeezing cases are collected worldwide to validate the efficacy of the proposed modeling paradigm. The results reveal that this paradigm achieves a prediction accuracy of 92.86% and a macro F₁-score of 0.9292. In particular, the individual F₁-scores on strong squeezing and extremely strong squeezing are more than 0.9, with excellent prediction reliability for high-intensity squeezing. Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. This study provides a valuable reference for surrounding rock squeezing classification under a limited data environment. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

18 pages, 9450 KB

Open AccessArticle

A Novel Axial Load Inversion Method for Rock Bolts Based on the Surface Strain of a Bearing Plate

by Yongchao Lei, Xingliang Xu, Suchuan Tian and Hao Shi

Mathematics 2024, 12(22), 3480; https://doi.org/10.3390/math12223480 - 7 Nov 2024

Cited by 3 | Viewed by 1967

Abstract

Anchor rock bolts are among the essential support components employed in coal mine support engineering. Measuring the axial load of the supporting anchor bolts constitutes an important foundation for evaluating the support effect and the mechanical state of the surrounding rock. The existing methods for measuring the axial load of rock bolts have difficulty meeting the actual demands in terms of accuracy and means. Therefore, we propose a novel inverse method for determining the axial load of rock bolts. On the basis of the dynamic relationship between the axial load of the anchor bolt and the strain of the plate, a calculation model for the inverse analysis of the axial load from the plate strain is presented, and it is verified and corrected through finite element analysis and indoor physical experiments. By combining the calculation model with the digital image correlation method, a low costinversion of the axial load of the anchor bolt in actual support engineering is achieved. The experimental results demonstrate that the average errors of the load inversion of anchor bolts in three different states via the theory and method proposed in this paper are less than 8.8% (4 kN), 3.6% (3.2 kN), and 14.7% (5.5 kN), respectively, and the average error of the axial load of the rock bolts in the proposed method is only 4.23 kN. It possesses relatively high accuracy and can be effectively applied in the actual production processes of mines. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 5672 KB

Open AccessReview

Development Status and Trend of Mine Intelligent Mining Technology

by Zhuo Wang, Lin Bi, Jinbo Li, Zhaohao Wu and Ziyu Zhao

Mathematics 2025, 13(13), 2217; https://doi.org/10.3390/math13132217 - 7 Jul 2025

Cited by 10 | Viewed by 4903

Abstract

Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

► Show Figures

Journal Menu

Journal Browser

Mathematical Modeling and Analysis in Mining Engineering

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (12 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI