Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry | ||
| Journal of Mining and Environment | ||
| مقاله 2، دوره 16، شماره 6، آذر و دی 2025، صفحه 1867-1879 اصل مقاله (1.88 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15869.3095 | ||
| نویسندگان | ||
| Chaimae Loudari* 1؛ Moha Cherkaoui1؛ Imad El Harraki1؛ Rachid Bennani2؛ Mohamed El Adnani3؛ EL Hassan Abdelwahed3؛ Intissar Benzakour4؛ François Bourzeix5؛ Karim Baina6 | ||
| 1LMAID Laboratory, National School of Mines of Rabat (ENSMR), Rabat, Morocco | ||
| 2Digitalization and Microelectronic Smart Devices Department, MAScIR, Rabat, Morocco | ||
| 3LISI Laboratory, Cadi Ayyad University (UCA), Marrakech, Morocco | ||
| 4Reminex Research Center, MANAGEM Group, Marrakech, Morocco | ||
| 5Embedded Systems and Artificial Intelligence Department, MAScIR, Rabat, Morocco | ||
| 6Al-Qualsadi Research and Development Team, ENSIAS, Mohammed V University, Rabat, Morocco | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Mining Grinding Mills؛ Process Optimization؛ Genetic Algorithm؛ Energy Efficiency؛ Metaheuristic Optimization | ||
| مراجع | ||
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