The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Sobouti, Arash; Hoseinian, Fatemeh Sadat; Rezai, Bahram; Jalili, Sara
• Is Part Of: Geosystem engineering. Volume 22:Issue 6(2019)
• Page Start: 319
• Page End: 327
• Publication Date: 2019-11-02
• Subjects: Prediction -- lead recovery -- lead concentrate -- leaching -- fluoroboric acid; Mining engineering -- Periodicals; Petroleum engineering -- Periodicals; Gas engineering -- Periodicals; Geology, Economic -- Periodicals; 620
• DOI: 10.1080/12269328.2019.1644205
• Languages: English
• ISSNs: 1226-9328

Abstract

ABSTRACT: Prediction of lead recovery during the leaching process is required to increase the process efficiency by proper modeling. In this study, a new artificial neural network predictive model based on the particle swarm optimization (ANN-PSO) was developed to predict the lead recovery by a hydrometallurgical method of lead concentrate leaching using fluoroboric acid. A multi-layer ANN-PSO model was trained for developing a predictive model based on the main effective parameters on the lead leaching process. The input parameters of the ANN-PSO model were leaching time, liquid/solid ratio, stirring speed, temperature and fluoroboric acid concentration, while the lead recovery during leaching was the output. The results indicate that the proposed ANN-PSO model can be effectively predicted the lead recovery during lead concentrate leaching using fluoroboric acid.