Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry

Loudari, Chaimae; Cherkaoui, Moha; El Harraki, Imad; Bennani, Rachid; El Adnani, Mohamed; Abdelwahed, EL Hassan; Benzakour, Intissar; Bourzeix, François; Baina, Karim

doi:10.22044/jme.2025.15869.3095

Document Type : Original Research Paper

Authors

¹ LMAID Laboratory, National School of Mines of Rabat (ENSMR), Rabat, Morocco

² Digitalization and Microelectronic Smart Devices Department, MAScIR, Rabat, Morocco

³ LISI Laboratory, Cadi Ayyad University (UCA), Marrakech, Morocco

⁴ Reminex Research Center, MANAGEM Group, Marrakech, Morocco

⁵ Embedded Systems and Artificial Intelligence Department, MAScIR, Rabat, Morocco

⁶ Al-Qualsadi Research and Development Team, ENSIAS, Mohammed V University, Rabat, Morocco

10.22044/jme.2025.15869.3095

Abstract

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.

Keywords

Main Subjects

Modelling & Simulation

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Journal of Mining and Environment

Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry

References

References

Volume 16, Issue 6
September and October 2025
Pages 1867-1879

Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry

References

References

Volume 16, Issue 6September and October 2025Pages 1867-1879

Volume 16, Issue 6
September and October 2025
Pages 1867-1879