Transforming mining energy optimization: a review of machine learning techniques and challenges
Mining is among the most energy-intensive industrial sectors, with processes such as drilling, crushing,and ore processing driving substantial operational costs and environmental impacts. Effective energymanagement is critical to addressing these challenges, particularly in the context of decarboniz...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1569716/full |
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| author | Sravani Parvathareddy Abid Yahya Lilian Amuhaya Ravi Samikannu Raymond Sogna Suglo |
| author_facet | Sravani Parvathareddy Abid Yahya Lilian Amuhaya Ravi Samikannu Raymond Sogna Suglo |
| author_sort | Sravani Parvathareddy |
| collection | DOAJ |
| description | Mining is among the most energy-intensive industrial sectors, with processes such as drilling, crushing,and ore processing driving substantial operational costs and environmental impacts. Effective energymanagement is critical to addressing these challenges, particularly in the context of decarbonizationtargets and the complexities of remote site operations. Machine Learning (ML) offers domain-specificopportunities for optimizing energy usage through predictive maintenance, demand forecasting, and realtime process control. This study presents a Scoping Systematic Literature Review (SSLR) of over 75recent publications focused on ML applications within mining energy systems. Techniques such as Random Forests, Neural Networks, and Long Short-Term Memory (LSTM) models demonstrate significant potential in enhancing operational efficiency, minimizing unplanned downtime, and reducing energy consumption. Advanced frameworks—including Reinforcement Learning and Digital Twins—further address mining-specific requirements such as fluctuating ore loads, harsh environmental conditions, and limited communication infrastructure. Despite increasing adoption, key challenges persist, including high implementation costs, limited interpretability, and the complexity of deploying ML in off-grid environments. The review identifies practical strategies to overcome these barriers, such as model compression for edge computing, federated learning for secure multi-site collaboration, and explainable AI for decision traceability. These findings provide targeted guidance for developing scalable, resilient, and energy-aware machine learning (ML) systems tailored to the unique operational demands of the mining sector and aligned with global sustainability goals. |
| format | Article |
| id | doaj-art-9dce89b8ab7b4bc289ffde10562f4815 |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-9dce89b8ab7b4bc289ffde10562f48152025-08-20T01:57:05ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-05-011310.3389/fenrg.2025.15697161569716Transforming mining energy optimization: a review of machine learning techniques and challengesSravani Parvathareddy0Abid Yahya1Lilian Amuhaya2Ravi Samikannu3Raymond Sogna Suglo4Department of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, BotswanaDepartment of Mining Engineering, Botswana International University of Science and Technology, Palapye, BotswanaMining is among the most energy-intensive industrial sectors, with processes such as drilling, crushing,and ore processing driving substantial operational costs and environmental impacts. Effective energymanagement is critical to addressing these challenges, particularly in the context of decarbonizationtargets and the complexities of remote site operations. Machine Learning (ML) offers domain-specificopportunities for optimizing energy usage through predictive maintenance, demand forecasting, and realtime process control. This study presents a Scoping Systematic Literature Review (SSLR) of over 75recent publications focused on ML applications within mining energy systems. Techniques such as Random Forests, Neural Networks, and Long Short-Term Memory (LSTM) models demonstrate significant potential in enhancing operational efficiency, minimizing unplanned downtime, and reducing energy consumption. Advanced frameworks—including Reinforcement Learning and Digital Twins—further address mining-specific requirements such as fluctuating ore loads, harsh environmental conditions, and limited communication infrastructure. Despite increasing adoption, key challenges persist, including high implementation costs, limited interpretability, and the complexity of deploying ML in off-grid environments. The review identifies practical strategies to overcome these barriers, such as model compression for edge computing, federated learning for secure multi-site collaboration, and explainable AI for decision traceability. These findings provide targeted guidance for developing scalable, resilient, and energy-aware machine learning (ML) systems tailored to the unique operational demands of the mining sector and aligned with global sustainability goals.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1569716/fullenergy managementmachine learningmining industrysustainabilitypredictive maintenanceenergy demand forecasting |
| spellingShingle | Sravani Parvathareddy Abid Yahya Lilian Amuhaya Ravi Samikannu Raymond Sogna Suglo Transforming mining energy optimization: a review of machine learning techniques and challenges Frontiers in Energy Research energy management machine learning mining industry sustainability predictive maintenance energy demand forecasting |
| title | Transforming mining energy optimization: a review of machine learning techniques and challenges |
| title_full | Transforming mining energy optimization: a review of machine learning techniques and challenges |
| title_fullStr | Transforming mining energy optimization: a review of machine learning techniques and challenges |
| title_full_unstemmed | Transforming mining energy optimization: a review of machine learning techniques and challenges |
| title_short | Transforming mining energy optimization: a review of machine learning techniques and challenges |
| title_sort | transforming mining energy optimization a review of machine learning techniques and challenges |
| topic | energy management machine learning mining industry sustainability predictive maintenance energy demand forecasting |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1569716/full |
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