Classification of Pb recovery effecting factors with artificial intelligence based algorithms

Abstract

Lead (Pb) is one of the most widely used non-ferrous materials. Many methods have been developed in order to produce the lead metal from initial raw materials or secondary resources. Process economy of the non-ferrous metal production is one of the major problems for the industry. In this study, an artificial neural network (ANN) based system was developed in order to provide detailed information regarding classification and estimation of Pb recovery effecting materials. The ANN structure was proposed with 4 inputs for predicting Pb recovery percentage. Four input parameters: temperature, NaCl concentration, time, and solid to liquid ratio values were selected based on physical considerations and the experimental test results. Analysis results showed that NaCl concentration and solid to liquid ratio are the most effective inputs on the recovery of Pb element from leach residue.