Аннотация
Energy consumption and process instability remain critical challenges in mining and mineral processing systems, where variations in material properties, equipment load, temperature, moisture content, and operating regimes directly affect productivity and resource efficiency. This paper presents a digital twin-based approach for optimizing energy consumption and improving process stability in mining and mineral processing operations. The proposed framework integrates real time process data, mathematical modeling, simulation-based prediction, and optimization procedures to support adaptive control of technological parameters. A digital representation of the production process is developed to reproduce the dynamic behavior of energy-intensive operations and to evaluate the influence of controllable factors on energy demand and operational stability. The model enables the identification of inefficient regimes, early detection of deviations, and selection of energy-saving control strategies without interrupting the actual production process. The results demonstrate that the use of a digital twin can improve decision-making accuracy, reduce unnecessary energy losses, and increase the reliability of process control in complex mining environments. The proposed approach provides a methodological basis for intelligent monitoring, predictive optimization, and sustainable digital transformation of mining enterprises.