Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning
Kenzhaliyev B. Imankulov T. Mukhanbet A. Kvyatkovskiy S. Dyussebekova M. Tasmurzayev N.
February 2025Multidisciplinary Digital Publishing Institute (MDPI)
Metals
2025#15Issue 2
The growing environmental impact of copper production necessitates innovative approaches for optimizing metallurgical processes and minimizing waste. This study addresses this challenge by leveraging advanced machine learning (ML) techniques to enhance the efficiency of pyrometallurgical operations such as slag optimization, composition prediction, and waste minimization. Using a combination of real-world and synthetic data, we developed models capable of both forward prediction, estimating slag and matte compositions from ore characteristics, and backward prediction, inferring ore compositions from output characteristics. Five ML algorithms were evaluated, with Gradient Boosting and Support Vector Regression demonstrating superior performance in capturing complex, non-linear relationships. Forward prediction achieved near-perfect accuracy, while backward prediction highlighted the inherent complexity of inverse modeling. This backward-driven strategy proposed in this research aims to determine optimal ore compositions to achieve desired outputs, reducing waste and energy consumption. By integrating ML models with a systematic hyperparameter optimization approach, this work advances the potential for sustainable and precise metallurgical processes. While challenges remain in refining backward predictions, the findings demonstrate the transformative potential of data-driven strategies in industrial metallurgy, paving the way for environmentally sustainable and economically efficient copper production practices.
backward prediction , copper production , Gradient Boosting , machine learning , metallurgical waste reduction , predictive modeling , pyrometallurgy , sustainable metallurgy
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Institute of Metallurgy and Ore Beneficiation, Satbayev University, Almaty, 050000, Kazakhstan
Faculty of Information Technologies, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Institute of Metallurgy and Ore Beneficiation
Faculty of Information Technologies
10 лет помогаем публиковать статьи Международный издатель
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