Energy Consumption Optimization in Mining Operations via Machine Learning Techniques

Mining operations are among the most energy-intensive industrial activities, with processes such as drilling, hauling, and mineral processing consuming vast amounts of power. Traditional energy optimization methods often fail to adapt to complex, dynamic operational conditions, leading to inefficiencies and increased costs. This study explores the application of Machine Learning (ML) techniques for optimizing energy consumption in mining, focusing on predictive modeling, anomaly detection, and adaptive control strategies. Our findings indicate that supervised learning models, including regression algorithms, random forest, and XGBoost, provide accurate energy consumption forecasts, enabling proactive energy management. Reinforcement learning demonstrates strong potential for real-time optimization, dynamically adjusting operational parameters to minimize energy waste. Deep learning models, particularly LSTMs and CNNs, are effective in predictive maintenance, reducing equipment failures and improving overall energy efficiency. Data pre-processing and feature engineering significantly influence ML model performance. Key parameters such as machine load, geological variations, and historical power usage impact predictive accuracy. Proper data handling techniques, including normalization and outlier removal, enhance model robustness. Evaluation metrics such as RMSE, MAE, and R² validate model accuracy, while classification metrics like precision and recall ensure reliable anomaly detection. This study innovatively integrates supervised, reinforcement, and deep learning techniques to optimize mining energy consumption dynamically. The research demonstrates the effectiveness of hybrid ML models in predictive maintenance and adaptive control, offering scalable solutions for mining sustainability. Future prospects include enhancing sensor integration, deploying edge computing for real-time energy management, and developing hybrid ML frameworks to further reduce operational costs and carbon footprint. Major Findings: This study demonstrates that hybrid machine learning techniques can significantly improve energy consumption forecasting and real-time optimization in mining. Reinforcement learning offers adaptive control benefits, while deep learning supports predictive maintenance, leading to substantial operational cost savings.