Shahrood University of Technology Journal of Mining and Environment 2251-8592 16 6 2025 10 01 Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry 1867 1879 3515 10.22044/jme.2025.15869.3095 EN Chaimae Loudari LMAID Laboratory, National School of Mines of Rabat (ENSMR), Rabat, Morocco Moha Cherkaoui LMAID Laboratory, National School of Mines of Rabat (ENSMR), Rabat, Morocco Imad El Harraki LMAID Laboratory, National School of Mines of Rabat (ENSMR), Rabat, Morocco Rachid Bennani Digitalization and Microelectronic Smart Devices Department, MAScIR, Rabat, Morocco Mohamed El Adnani LISI Laboratory, Cadi Ayyad University (UCA), Marrakech, Morocco EL Hassan Abdelwahed LISI Laboratory, Cadi Ayyad University (UCA), Marrakech, Morocco Intissar Benzakour Reminex Research Center, MANAGEM Group, Marrakech, Morocco François Bourzeix Embedded Systems and Artificial Intelligence Department, MAScIR, Rabat, Morocco Karim Baina Al-Qualsadi Research and Development Team, ENSIAS, Mohammed V University, Rabat, Morocco Journal Article 2025 04 10 Energy efficiency and product quality control are critical concerns in grinding mill operations, particularly within the innovative context of Mine 4.0. This study introduces a novel Genetic Algorithm (GA)-based optimization framework specifically developed to address these challenges. Given the mining industry’s significant energy consumption, especially in grinding processes, the proposed approach optimizes key parameters such as feed composition, water flow rates, and power consumption levels, while maintaining sieve refusal near the target threshold of 20%. Using real operational data from a Moroccan plant, the GA achieved a Mean Absolute Error (MAE) of 0.47, outperforming Simulated Annealing (SA) and Particle Swarm Optimization (PSO), which yielded MAEs of 1.14 and 0.74, respectively. The GA also demonstrated superior convergence stability and robustness, as evidenced by lower variability in predicted power consumption. These results validate the effectiveness of the GA framework in navigating nonlinear, high-dimensional parameter spaces and improving energy efficiency while ensuring product quality consistency. Ultimately, this research confirms the potential of metaheuristic optimization in enhancing grinding mill efficiency and supports the broader shift towards intelligent and sustainable mining operations under the Mine 4.0 paradigm. https://jme.shahroodut.ac.ir/article_3515_9e5b5c38a2eb8b5a8d19c8d3e0d406e1.pdf