Prediction of lead recovery from waste battery paste by using intelligent models: Application to lead-acid battery recycling

This work highlights the application of two intelligent models including radial basis function networks optimized by genetic algorithm (GA-RBF) and least square support vector machine optimized by coupled simulated annealing (CSA-LSSVM) for prediction of lead remained in solution during a specific hydrometallurgical process based on experimental data gathered from literature. The results of various models were evaluated by different statistical and graphical methods and it was concluded that the GA-RBF model presents more accurate and reliable results and predictions compared to other models. Results of this work also showed that intelligent models are robust tools which their accurate prediction capabilities could be considered in lead acid-battery recycling processes to predict the amount of lead recovery and evaluate the efficiency and effectiveness of various processes.

کلیدواژه ها:

Lead-acid battery ، Recycling ، Intelligent models