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Hybrid Model of Mass-Concentration Conservation and Neural Network for Zinc Leaching

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Abstract

To realize the optimal control of the zinc leaching process, an accurate mathematical model is necessary. However, the modeling of zinc leaching process faces the challenges of complex reaction rate, low data quality and error transmission of cascade modeling. To solve these problems, a hybrid model of mass-concentration conservation and neural network (MCCNN) is proposed. First, the method of integral regularization is introduced to process data and reduce the influence of measurement noise. Then, a model based on mass-concentration conservation mechanism model and data-driven model is proposed to build sub-models of each unit, which can solve the problems of insufficient data and complex reaction rate. Finally, a parallel error compensation structure is proposed to alleviate the error transfer caused by the cascade of sub-models. This hybrid framework provides a feasible and effective solution for zinc leaching process modeling, and its effect is verified by numerical simulation and zinc leaching process case.

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Acknowledgements

The authors acknowledge National Natural Science Foundation of China (Grant No. 92167105) and the graduate students of Central South University for the independent exploration and innovation project (2022ZZTS0684).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Can Zhou, Cihang Cheng, Keke Huang & Chunhua Yang

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  1. Can Zhou
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  2. Cihang Cheng
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  3. Keke Huang
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  4. Chunhua Yang
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Correspondence to Keke Huang.

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Zhou, C., Cheng, C., Huang, K. et al. Hybrid Model of Mass-Concentration Conservation and Neural Network for Zinc Leaching. JOM 75, 1684–1694 (2023). https://doi.org/10.1007/s11837-023-05755-0

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