Linear Genetic Programming for Prediction of Nickel Recovery from Spent Nickel Catalyst

Abstract

Problem statement: In this study Linear Genetic Programming (LGP) and statistical regression are used in predicting Current Efficiency (CE) of Electro deposition cell used for recovery of nickel from spent nickel catalyst. Approach: The Nickel electro deposition from spent catalyst leachate solutions was studied to determine the effect of the operative conditions such as nickel concentration, temperature, current density and time on the CE of the unit cell. Results: For this purpose, LGP and regression models were calibrated with training sets and validated by testing sets. Additionally, the robustness of the proposed LGP and regression models were evaluated by experimental data, which are used neither in training nor at testing stage. The results showed that both techniques predicted the CE data in quite good agreement with the observed ones and the predictions of LGP are challenging. Conclusion/Recommendations: The performance of LGP, which was moderately better than statistical regression, is very promising and hence supports the use of LGP in simulating the electro deposition of Nickel from spent Nickel catalyst.