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Artificial Intelligence for Monitoring and Optimization of an Integrated Mineral Processing Plant

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Abstract

Recovery of the mineral and grade of the product in an integrated mineral processing plant are two key performance indicators that define plant profitability. Online monitoring and optimization of these parameters helps improve process performance in real-time. However, achieving high product grade and high mineral recovery simultaneously is challenging due to their conflicting nature. We have applied machine-learning and deep-learning algorithms to build models for predicting recovery and grade on hourly and daily basis. We have further formulated and solved a multi-objective optimization problem maximizing recovery and grade to obtain a pareto optimal solution using a non-dominated sorting-based evolutionary algorithm, NSGA-II. The results obtained are useful in identifying the operability of a mineral processing plant to achieve the optimum grade and recovery for a given feed grade and the processing circuit.

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Acknowledgements

The authors thank the management of Tata Consultancy Services Limited to publish this paper, and Mr. K. Ananth Krishnan, Dr. Gautam Shroff and Dr. Pradip for their encouragement.

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Authors and Affiliations

  1. TCS Research, Tata Consultancy Services Ltd., Pune, 411013, India

    Vishnu Swaroopji Masampally, Aditya Pareek, Naga Ravikumar Varma Nadimpalli & Venkataramana Runkana

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  1. Vishnu Swaroopji Masampally
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  2. Aditya Pareek
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  3. Naga Ravikumar Varma Nadimpalli
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Correspondence to Venkataramana Runkana.

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Masampally, V.S., Pareek, A., Nadimpalli, N.R.V. et al. Artificial Intelligence for Monitoring and Optimization of an Integrated Mineral Processing Plant. Trans Indian Inst Met (2023). https://doi.org/10.1007/s12666-023-03093-y

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