Review of Transition from Mining 4.0 to Mining 5.0 Innovative Technologies
Source abstract
Abstract
The sustainable provision of mankind with energy and mineral raw materials is associated with an increase not only in industrial but also in the ecological and economic development of the raw material sector. Expanding demand for energy, metals, building and chemical raw materials on the one hand, and the deterioration of the living environment along with a growth of raw materials extraction on the other, put the human-centric development of mining at the forefront. This forms a transition trend from Mining 4.0 technologies such as artificial intelligence, big data, smart sensors and robots, machine vision, etc., to Mining 5.0, presented with collaborative robots and deserted enterprises, bioextraction of useful minerals, postmining, and revitalization of mining areas. This “bridge” is formed by the technological convergence of information, cognitive, and biochemical technologies with traditional geotechnology, which should radically change the role of the resource sector in the economy and society of the 21st century. The transition from Mining 3.0 to 4.0 cannot be considered complete. However, at the same time, the foundation is already being laid for the transition to Mining 5.0, inspired, on the one hand, by an unprecedented gain in productivity, labor safety, and predictability of commodity markets, on the other hand, by the upcoming onset of Industry 5.0. This review provides a multilateral observation of the conditions, processes, and features of the current transition to Mining 4.0 and the upcoming transformation on the Mining 5.0 platform, highlighting its core and prospects for replacing humans with collaborated robots and artificial intelligence. In addition, the main limitations of the transition to Mining 5.0 are discussed, the overcoming of which is associated with the development of green mining and ESG (environment, social, and governance) investment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mineral Processing and Grinding · Mining Techniques and Economics · Mining and Gasification Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering Entrepreneurship, National Research Tomsk Polytechnic University, 30 Lenina St., 634050 Tomsk, Russia
Department of Political Economy and History of Economic Science, Plekhanov Russian University of Economics, 36 Stremyanny Lane, 117997 Moscow, Russia
Funding
Russian Science Foundation
References (100+)
Show source-provided references
1. World Economic Forum (2023, March 17). Digital Transformation Initiative. Mining and Metals Industry. Available online: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-mining-and-metals-white-paper.pdf.
2. Zhironkin, S., Gasanov, M., and Suslova, Y. (2022). Orderliness in Mining 4.0. Energies, 15.10.3390/en15218153 ↗
3. Principles, factors and types of structural transformation of the economy in the light of the human capital reproduction10.26730/2587-5574-2022-4-4-17 ↗
4. Nahavandi, S. (2019). Industry 5.0—A Human-Centric Solution. Sustainability, 11.10.3390/su11164371 ↗
5. Rossmann, A. (2018, January 13–16). Digital Maturity: Conceptualization and Measurement Model. Proceedings of the Bridging the Internet of People, Data, and Things: 39th International Conference on Information Systems (ICIS 2018), San Francisco, CA, USA.
6. Clausen, E., Sorensen, A., Uth, F., and Mitra, R. (2020). Assessment of the Effects of Global Digitalization Trends on Sustainability in Mining, Federal Institute for Geosciences and Natural Resources.
7. Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies
8. Massaro, A. (2022). Advanced Control Systems in Industry 5.0 Enabling Process Mining. Sensors, 22.10.3390/s22228677 ↗
9. Hossein, M.N., Mohammadrezaei, M., Hunt, J., and Zakeri, B. (2020). Internet of Things (IoT) and the Energy Sector. Energies, 13.10.3390/en13020494 ↗
10. The circular economy and Industry 4.0: Synergies and challenges
11. Raut, R., Krit, S., and Chatterje, P. (2022). Machine Vision for Industry 4.0. Applications and Case Studies, CRC Press.10.1201/9781003122401 ↗
12. Future of metal mining: Sixteen predictions10.1504/IJMME.2009.027259 ↗
13. Digital Transformation in the Mining Sector: Exploring Global Technology Trends and Managerial Issues10.1051/e3sconf/202131504006 ↗
14. Laloui, L., and Rotta Loria, A.F. (2020). Analysis and Design of Energy Geostructures. Theoretical Essentials and Practical Application, Academic Press.10.1016/B978-0-12-816223-1.00002-3 ↗
15. Elhoseny, M., and Yuan, X. (2022). Studies in Distributed Intelligence, Springer.
16. Development of Mining in the Conditions of Industry 4.0: New Challenges and Opportunities
17. Carrasco, Y. (2022, January 7–11). Mining 4.0: A Digital Transformation Approach to Mining Sector. Proceedings of the Conference “Technology Management and Leadership in Digital Transformation—Looking Ahead to Post-COVID Era”, Portland, OR, USA.
18. Nad, A., Jooshaki, M., Tuominen, E., Michaux, S., Kirpala, A., and Newcomb, J. (2022). Digitalization Solutions in the Mineral Processing Industry: The Case of GTK Mintec; Finland. Minerals, 12.10.3390/min12020210 ↗
19. The challenges of Industry 4.0 and the need for new answers in the mining industry
20. Digital technologies of industry 4.0 in mining 4.0—Prospects for the development of geotechnology in the XXI century
21. Industry 4.0 in development of new technologies for underground mining10.1051/e3sconf/202017401002 ↗
22. Assessment of the Need to Create Control System of Unmanned Dump Truck10.1051/e3sconf/202017703022 ↗
23. Design and Implementation of Intelligent Energy Monitoring System for Industrial Machines
24. Staszewski, W., and Jablonski, A. (2019, January 1–5). Challenges in application of MEMS sensors for condition monitoring of machinery. Proceedings of the Conference Diagnostyka Maszyn: XLVI Ogólnopolskie Sympozjum, Katowice, Poland.
25. Big Data in geophysics and other geosciences
26. Cehlár, M., Janočko, J., Šimková, Z., Pavlik, T., Tyulenev, M., Zhironkin, S., and Gasanov, M. (2019). Mine Sited after Mine Activity: The Brownfields Methodology and Kuzbass Coal Mining Case. Resources, 8.10.3390/resources8010021 ↗
27. Introduction of an Assistance System to Support Domain Experts in Programming Low-Code to Leverage Industry 5.010.1109/LRA.2022.3193728 ↗
28. Recent development on underground coal gasification and subsequent CO2 storage10.1016/j.joei.2015.05.004 ↗
29. Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing10.1016/j.eng.2019.07.015 ↗
30. Cyber-physical systems and human in the context of intelligent production of Industry 4.0
31. Application of intelligent management technologies and business design of Industry 5.0 in Mining 5.0
32. Crespo Marquez, A., de la Fuente Carmona, A., and Antomarioni, S. (2019). A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency. Energies, 12.10.3390/en12183454 ↗
33. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models10.1016/j.renene.2015.03.023 ↗
34. Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty10.3390/mining2010003 ↗
35. Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems10.3390/mining2010008 ↗
36. Zhang, M., Zhang, Y., Zhou, M., Jiang, K., Shi, H., Yu, Y., and Hao, N. (2021). Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt. Appl. Sci., 11.10.3390/app11167282 ↗
37. One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling10.3390/mining1030019 ↗
38. Brodny, J., and Tutak, M. (2020). The Use of Artificial Neural Networks to Analyze Greenhouse Gas and Air Pollutant Emissions from the Mining and Quarrying Sector in the European Union. Energies, 13.10.3390/en13081925 ↗
39. Meng, X., Chang, H., and Wang, X. (2022). Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis. Energies, 15.10.3390/en15062262 ↗
40. Application of Artificial Neural Network (ANN) for Prediction and Optimization of Blast-Induced Impacts10.3390/mining1030020 ↗
41. Zhang, K., Zhu, J., He, M., Jiang, Y., Zhu, C., Li, D., Kang, L., Sun, J., Chen, Z., and Wan, X. (2022). Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model. Energies, 15.10.3390/en15093292 ↗
42. Prediction of Mining Conditions in Geotechnically Complex Sites10.3390/mining1030018 ↗
43. Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach10.3390/mining2020013 ↗
44. Zhang, C., Wang, X., Fang, S., and Shi, X. (2022). Construction and Application of VR-AR Teaching System in Coal-Based Energy Education. Sustainability, 14.10.3390/su142316033 ↗
45. Kuzbass Intersectoral Center for Labor Protection (2023, March 17). VR-Simulators/3D-Simulators on the ProExpVR Platform. Available online: https://kuzbasscot.ru/virtualnye-trenazhery/.
46. DT Consulting (2023, March 17). Case: VR Technologies in the Training Center of the Mine. Available online: https://www.dtconsulting.ru/case-vr-mine.
47. Babkov, V.S. (2023, March 01). Development of the Virtual Mine Silent Complex Based on the Microsoft Kinect Platform—A Presentation. Available online: http://www.myshared.ru/slide/491418/.
48. The software system for building a virtual working environment at mining enterprises
49. Krajčovič, M., Gabajová, G., Furmannová, B., Vavrík, V., Gašo, M., and Matys, M. (2021). A Case Study of Educational Games in Virtual Reality as a Teaching Method of Lean Management. Electronics, 10.10.3390/electronics10070838 ↗
50. Digital transformations in drive control technology for exploration and mining equipment
51. Joseph, A., and Balachandra, P. (2020). Energy Internet; the Future Electricity System: Overview, Concept, Model Structure and Mechanism. Energies, 13.10.3390/en13164242 ↗
52. Park, S., Jung, D., Nguyen, H., and Choi, Y. (2021). Diagnosis of Problems in Truck Ore Transport Operations in Underground Mines Using Various Machine Learning Models and Data Collected by Internet of Things Systems. Minerals, 11.10.3390/min11101128 ↗
53. Union, I.G. (2020). The Challenge of Industry 4.0 and the Demand for New Answers, IndustriALL Head Office.
54. A Review of Digital Transformation in Mining
55. SWEclat: A frequent itemset mining algorithm over streaming data using Spark Streaming10.1007/s11227-020-03190-5 ↗
56. Schindler, M., Schoone, S., and Clausen, E. (2023, January 26–30). Towards an Evolving Software Ecosystem in the Mining Industry. Proceedings of the Twelfth International Conference on Adaptive and Self-Adaptive Systems and Applications, Nice, France.
57. The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems
58. Mining 4.0—The Impact of New Technology from a Work Place Perspective
59. Smith, K., and Sepasgozar, S. (2022). Governance, Standards and Regulation: What Construction and Mining Need to Commit to Industry 4.0. Buildings, 12.10.3390/buildings12071064 ↗
60. A Study on Industrial IoT for the Mining Industry: Synthesized Architecture and Open Research Directions
61. Carrasco, Y. (2019, January 7–11). Technology and Innovation Management in Open-Pit Peruvian Mining: Case Studies. Proceedings of the 28th International Conference for Management of Technology (IAMOT 2019). National Institute of Industrial Engineering, Mumbai, India.
62. Enabling Digital Twins in Industry 4.0
63. SAP SE (2023, March 17). The Intelligent Enterprise for the Mining Industry Making Mining More Agile and Sustainable from the Pit to the Customer. Available online: https://www.sap.com/documents/2017/11/3eb0f65b-dd7c-0010-82c7-eda71af511fa.html.
64. Guidance for Materials 4.0 to interact with a digital twin10.1017/dce.2022.23 ↗
65. Digital Twin for Industry 4.010.3389/frvir.2022.968054 ↗
66. A Hybrid Method integrating Industry 4.0’s Energy Digitization10.37394/23202.2022.21.17 ↗
67. A Knowledge Graph System for the Maintenance of Coal Mine Equipment
68. Dli, M., Puchkov, A., Meshalkin, V., Abdeev, I., Saitov, R., and Abdeev, R. (2020). Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach. Energies, 13.10.3390/en13215829 ↗
69. Wang, X., Yao, F., and Wen, F. (2022). Applications of Blockchain Technology in Modern Power Systems: A Brief Survey. Energies, 15.10.3390/en15134516 ↗
70. Pincheira, M., Antonini, M., and Vecchio, M. (2022). Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems. Sensors, 22.10.3390/s22030899 ↗
71. Huang, Y., Fan, J., Yan, Z., Li, S., and Wang, Y. (2022). A Gas Concentration Prediction Method Driven by a Spark Streaming Framework. Energies, 15.10.3390/en15155335 ↗
72. Walczak, R., Koszewski, K., Olszewski, R., Ejsmont, K., and Kálmán, A. (2023). Acceptance of IoT Edge-Computing-Based Sensors in Smart Cities for Universal Design Purposes. Energies, 16.10.3390/en16031024 ↗
73. Kacprzyk, J. (2019). Intelligent Systems in Production Engineering and Maintenance, Springer.
74. Liu, Y., Liu, Z., Gao, K., Huang, Y., and Zhu, C. (2022). Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network. Sensors, 22.10.3390/s22062096 ↗
75. Wajs, J., Trybala, P., Gorniak-Zimroz, J., Krupa-Kurzynowska, J., and Kasza, D. (2021). Modern Solution for Fast and Accurate Inventorization of Open-Pit Mines by the Active Remote Sensing Technique—Case Study of Mikoszów Granite Mine (Lower Silesia; SW Poland). Energies, 14.10.3390/en14206853 ↗
76. Monitoring Mining Subsidence Using A Combination of Phase-Stacking and Offset-Tracking Methods10.3390/rs70709166 ↗
77. Hu, L., Tomás, R., Tang, X., López Vinielles, J., Herrera, G., Li, T., and Liu, Z. (2023). Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets. Remote Sens., 15.10.3390/rs15040996 ↗
78. Du, S., Wang, Y., Zheng, M., Zhou, D., and Xia, Y. (2019). Goaf Locating Based on InSAR and Probability Integration Method. Remote Sens., 11.10.3390/rs11070812 ↗
79. Peng, Y., Zhang, Z., He, G., and Wei, M. (2019). An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images. Remote Sens., 11.10.3390/rs11080987 ↗
80. Gabrys, M., and Ortyl, L. (2020). Georeferencing of Multi-Channel GPR—Accuracy and Efficiency of Mapping of Underground Utility Networks. Remote Sens., 12.10.3390/rs12182945 ↗
81. Shen, W., Wang, S., Lin, Y., Li, Y., Ding, F., and Wang, Y. (2022). Ground-Based SAR Moving Target Refocusing Based on Relative Speed for Monitoring Mine Slopes. Remote Sens., 14.10.3390/rs14174243 ↗
82. Du, S., Mallorqui, J.J., Fan, H., and Zheng, M. (2020). Improving PSI Processing of Mining Induced Large Deformations with External Models. Remote Sens., 12.10.3390/rs12193145 ↗
83. (2023, March 17). Rio Tinto Smart Mining. Available online: https://www.riotinto.com/about/innovation/smart-mining.
84. How to apply the ERP model for Smart Mining?10.1051/matecconf/202236801015 ↗
85. The role of big data in Industry 4.0 in mining industry in Serbia
86. Implementation of Industry 4.0 technologies in the mining industry—A case study10.1504/IJMME.2020.105852 ↗
87. Big Data and Energy Security: Impacts on Private Companies; National Economies and Societies
88. Duan, M., Huang, Q., Xu, R., Wang, C., and Xu, J. (2022). Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G). Machines, 10.10.3390/machines10090733 ↗
89. Li, S., Chen, J., and Liu, C. (2022). Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data. Minerals, 12.10.3390/min12050616 ↗
90. Liu, C., Chen, J., Li, S., and Qin, T. (2022). Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province. Minerals, 12.10.3390/min12060669 ↗
91. Stupar, D.I., Ogrizovic, V., Roser, J., and Vizintin, G. (2022). Analytical and Numerical Solution for Better Positioning in Mines with Potential Extending Application in Space Mining. Minerals, 12.10.3390/min12050640 ↗
92. Lim, J.-H., Kim, H.-W., Hong, S., Oh, J.-W., and Bae, D.-S. (2022). Simulation Technology Development for Dynamic Analysis of Mechanical System in Deep-Seabed Integrated Mining System Using Multibody Dynamics. Minerals, 12.10.3390/min12050498 ↗
93. Zhou, B., Chang, P., and Xu, G. (2019). Computational Fluid Dynamic Simulation of Inhaled Radon Dilution by Auxiliary Ventilation in a Stone-Coal Mine Laneway and Dosage Assessment of Miners. Processes, 7.10.3390/pr7080515 ↗
94. Xie, Z., Zhang, N., Meng, F., Han, C., An, Y., and Zhu, R. (2019). Deformation Field Evolution and Failure Mechanisms of Coal–Rock Combination Based on the Digital Speckle Correlation Method. Energies, 12.10.3390/en12132511 ↗
95. Kaminski, P., Dyczko, A., and Prostanski, D. (2021). Virtual Simulations of a New Construction of the Artificial Shaft Bottom (Shaft Safety Platform) for Use in Mine Shafts. Energies, 14.10.3390/en14082110 ↗
96. Rozmus, M., Tokarczyk, J., Michalak, D., Dudek, M., Szewerda, K., Rotkegel, M., Lamot, A., and Roser, J. (2021). Application of 3D Scanning; Computer Simulations and Virtual Reality in the Redesigning Process of Selected Areas of Underground Transportation Routes in Coal Mining Industry. Energies, 14.10.3390/en14092589 ↗
97. Janus, J., and Ostrogorski, P. (2022). Underground Mine Tunnel Modelling Using Laser Scan Data in Relation to Manual Geometry Measurements. Energies, 15.10.3390/en15072537 ↗
98. Xue, G., Li, R., Liu, S., and Wei, J. (2022). Research on Underground Coal Mine Map Construction Method Based on LeGO-LOAM Improved Algorithm. Energies, 15.10.3390/en15176256 ↗
99. Wang, J., Zhao, H., Bi, L., and Wang, L. (2018). Implicit 3D Modeling of Ore Body from Geological Boreholes Data Using Hermite Radial Basis Functions. Minerals, 8.10.3390/min8100443 ↗
100. Martelet, G., Gloaguen, E., Dossing, A., Lima Simoes da Silva, E., Linde, J., and Rasmussen, T.M. (2021). Airborne/UAV Multisensor Surveys Enhance the Geological Mapping and 3D Model of a Pseudo-Skarn Deposit in Ploumanach; French Brittany. Minerals, 11.10.3390/min11111259 ↗