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 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.