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Modeling of manganese recovery from waste Li-ion batteries by gene expression programming

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Abstract

Modeling of leaching process as a function of operational variables can specify an accurate perspective in the forecasting of valuable metals’ recovery from waste lithium-ion batteries (LIBs). Gene-expression programming algorithm (GEP), an innovative progressed computing strategy, was utilized to estimate the recovery efficiency of manganese from waste LIBs. Numerous tests were performed through different leaching parameters, i.e., the concentration of reagents (Cr: H2SO4 and H2O2), the solid/liquid ratio (ρS/L), temperature (Tr), and experiment time (τr) as inputs and the manganese recovery as the corresponding GEP model output. The capability of the GEP model to anticipate and formulate leaching reaction was compared with response surface modeling (RSM). Results showed that the GEP model with mean R2 and MSE (mean square error) values of 0.982 and 0.01 is more accurate in manganese leaching prediction. The approach proposes a mathematical expression to describe the complicated relationship between the manganese recovery and the reaction parameters accurately. Furthermore, sensitivity analysis revealed that the solid/liquid ratio (ρS/L) and the leaching time were the foremost affecting variables on the manganese recovery.

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Authors and Affiliations

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

    Hossein Ebrahimzade

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

    Gholam Reza Khayati

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

    Mahin Schaffie

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  1. Hossein Ebrahimzade
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Correspondence to Hossein Ebrahimzade.

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Ebrahimzade, H., Khayati, G.R. & Schaffie, M. Modeling of manganese recovery from waste Li-ion batteries by gene expression programming. J Mater Cycles Waste Manag 23, 2218–2231 (2021). https://doi.org/10.1007/s10163-021-01285-6

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