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Journal of Mining and Environment، جلد ۱۶، شماره ۶، صفحات ۱۸۶۷-۱۸۷۹
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عنوان انگلیسی Optimization of Grinding Mill Parameters using Genetic Algorithms for Energy Efficiency in Mining Industry
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Mining Grinding Mills,Process Optimization,Genetic Algorithm,Energy Efficiency,Metaheuristic Optimization
نویسندگان مقاله 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

نشانی اینترنتی https://jme.shahroodut.ac.ir/article_3515_9e5b5c38a2eb8b5a8d19c8d3e0d406e1.pdf
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