An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

Abstract

In this study, a hybrid of support vector regression and a modified tunicate swarm algorithm (SVR-MTSA) strategy is developed to optimize the process parameters for recovery of Pb from the residual of the liberator cells. The lead recovery efficiency in the selected process was strongly nonlinear and depended on several process parameters including temperature, processing time, the content of coke, Na2CO3, and Fe in the precursor. The results confirmed a good agreement between the efficiencies obtained experimentally and those predicted by the model. It is also shown that using the optimal process parameters suggested by the model, achieving a Pb recovery of more than 99% was possible. Sensitivity analysis using the proposed SVR-MTSA model revealed that temperature, coke content, processing time, Na2CO3 amount, and Fe content of the raw material had the most significant effect on the efficiency of the lead recovery, respectively.

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Correspondence to Gholam Reza Khayati.

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Abdolinejhad, F., Khayati, G.R., Raiszadeh, R. et al. An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm. J Mater Cycles Waste Manag (2021). https://doi.org/10.1007/s10163-021-01256-x

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Keywords

  • Lead recovery efficiency
  • Modified tunicate swarm algorithm
  • Support vector regression
  • Lead residual of liberator cell
  • Optimization