A novel predictive model for estimation of cobalt leaching from waste Li-ion batteries: Application of genetic programming for design

Abstract

Leaching process is one of the most influential steps during waste lithium-ion batteries (LIBs) recycling. Therefore, the employment of beneficial reaction modeling strategies assists to distinguish and predict the behavior of operational parameters and optimized efficiency. In this study, a gene-expression programming (GEP), i.e., a new evolutionary computing approach, was applied for the prediction of cobalt leaching from waste LIBs using H2SO4 in the presence of H2O2. Several leaching experiments were carried out by consideration of the reagent concentration (Cr), the solid-liquid ratio (S/L), reaction temperature (Tr) and time (τr) as input parameters and leached cobalt percentage as output variable. The GEP-based models were able to predict the leaching of cobalt with a mean standard error (MSE) of less than 0.1 and mean R-square of 0.979. Results affirmed that the proposed model can be a powerful tool in prediction and generation of a mathematical expression for illustration of the relationship between the leaching reaction parameters and the leached percentage. Moreover, the sensitivity analysis showed that the sulfuric acid concentration and S/L ratio were the most influencing parameters on the cobalt leaching from the waste LIBs, respectively.