Transforming mining energy optimization - a review of machine learning techniques and challenges

Aug 4, 2026

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Mining is among the most energy-intensive industrial sectors, where drilling, crushing, and ore processing drive high costs and environmental impacts. Energy management is critical under decarbonization targets and the operational constraints of remote sites. This scoping systematic literature review (SSLR) synthesizes 75+ recent studies on machine learning for mining energy systems, covering predictive maintenance, demand forecasting, and real-time control. It also addresses frameworks like reinforcement learning and digital twins, while outlining persistent deployment challenges and mitigation strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

1 Introduction

Energy-intensive mining and the need for data-driven approaches

ML methods for prediction, control, and maintenance

Barriers to deployment in mining environments

2 Review scope and methodology

Scoping systematic literature review (SSLR) overview

3 Machine learning techniques for mining energy optimization

Predictive maintenance and anomaly forecasting

Demand forecasting and process optimization

Deep learning and sequence modeling (e.g., LSTM)

4 Advanced frameworks and enabling technologies

Reinforcement learning and dynamic control

Digital twins for mining-specific requirements

Edge computing and real-time feedback

5 Challenges and future directions

Implementation cost and interpretability

Deployment complexity in off-grid environments

Mitigation strategies (compression, federated learning, explainable AI)

TYPE Review PUBLISHED 26 May 2025 DOI 10.3389/fenrg.2025.1569716 OPEN ACCESS EDITED BY Shaohua Wu, Dalian University of Technology, China REVIEWED BY Brenno Menezes, Hamad bin Khalifa University, Qatar Xu Han, University of Illinois Chicago, United States Lu Dong, Yangtze University, China *CORRESPONDENCE Sravani Parvathareddy, [sravani.bavana@gmail.com](sravani.bavana@gmail.com) RECEIVED 01 February 2025 ACCEPTED 29 April 2025 PUBLISHED 26 May 2025 CITATION Parvathareddy S, Yahya A, Amuhaya L, Samikannu R and Suglo RS (2025) Transforming mining energy optimization: areview of machine learning techniques and challenges. Front. Energy Res. 13:1569716 . doi: 10.3389/fenrg.2025.1569716 COPYRIGHT © 2025 Parvathareddy, Yahya, Amuhaya, Samikannu and Suglo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Transforming mining energy optimization: a review of machine learning techniques and challenges Sravani Parvathareddy 1*, Abid Yahya 1, Lilian Amuhaya 1, Ravi Samikannu 1 and Raymond Sogna Suglo 2 1 Department of Electrical and Communications Systems Engineering, Botswana International University of Science and Technology, Palapye, Botswana, 2 Department of Mining Engineering, Botswana International University of Science and Technology, Palapye, Botswana 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 ShortTerm 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. KEYWORDS energy management, machine learning, mining industry, sustainability, predictive maintenance, energy demand forecasting, process optimization, deep learning 1 Introduction The mining industry is one of the most energy-intensive sectors, responsible for over 10% of global industrial energy consumption. Crushing and grinding processes alone can account for nearly 50% of a mine’s total energy use (Bhatia et al., 2023). This high demand, combined with rising sustainability targets and decarbon

FAQs

What is the document’s main focus regarding mining energy?

It focuses on using machine learning to optimize energy in mining, emphasizing predictive maintenance, demand forecasting, and real-time process control.

Which machine learning techniques are highlighted as promising for mining energy systems?

Random Forests, Neural Networks, and Long Short-Term Memory (LSTM) models are highlighted for improving operational efficiency, reducing downtime, and lowering energy consumption.

What challenges remain for deploying ML in mining, especially off-grid sites?

Persistent challenges include high implementation costs, limited interpretability, and the complexity of deploying ML in off-grid environments with constrained connectivity and harsh conditions.

Aug 4, 2026

Upload by Skyler