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PSO–ANN-based prediction of cobalt leaching rate from waste lithium-ion batteries

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Abstract

Leaching is a complex solid–liquid reaction which has an important influence on the recovery efficiency of the spent lithium-ion batteries (LIBs). Therefore, it is of significant importance to utilize an appropriate technique to predict the effect of operating parameters on the optimized recovery rate. In the present study, a combined method of the artificial neural network (ANN) and particle swarm optimization algorithm (PSO) was used as a model to predict the leaching efficiency of cobalt from spent LIBs. To find the dependency of the leached percentage of cobalt on the operational parameters as model inputs, 42 repeatable numerous experiments are performed using H2SO4 in the presence of H2O2. It was found that the proposed model can be a useful technique in the demonstration of the nonlinear relationship between the leaching efficiency and the process parameters. The performance of PSO–ANN models was validated by statistical thresholds and compared with those of common ANN technique. Moreover, it was found that the pulp density of the leaching solution and the concentration of sulfuric acid were the most important reaction parameters of the spent LIBs recovery, respectively.

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Authors and Affiliations

  1. Mineral Industries Research Center (MIRC), Shahid Bahonar University of Kerman, PO Box No. 76135-133, Kerman, Iran

    Hossein Ebrahimzade

  2. Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, PO Box No. 76135-133, Kerman, Iran

    Gholam Reza Khayati

  3. Department of Chemical Engineering, Shahid Bahonar University of Kerman, PO Box No. 76135-133, Kerman, Iran

    Mahin Schaffie

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  1. Hossein Ebrahimzade
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  2. Gholam Reza Khayati
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  3. Mahin Schaffie
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Correspondence to Hossein Ebrahimzade.

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Ebrahimzade, H., Khayati, G.R. & Schaffie, M. PSO–ANN-based prediction of cobalt leaching rate from waste lithium-ion batteries. J Mater Cycles Waste Manag 22, 228–239 (2020). https://doi.org/10.1007/s10163-019-00933-2

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  • DOI: https://doi.org/10.1007/s10163-019-00933-2

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