A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: application of gene expression programming

  • Samad Ghasemi Assistant Professor of Department of Metallurgy and Materials Engineering, Hamedan University of Technology, Hamedan, I. R. Iran https://orcid.org/0000-0001-6833-3547
  • Shima Vaghar MSc student, Department of Metallurgy and Materials Engineering, Hamedan University of Technology, Hamedan, I.R. Iran
  • Mehran Pourzafar MSc student, Department of Mining Engineering, Hamedan University of Technology, Hamedan, I. R. Iran
  • Hesam Dehghani Assistant Professor of Mining Engineering, Hamedan University of Technology, Hamedan, I. R. Iran https://orcid.org/0000-0003-2029-9540
  • Akbar Heidarpour Assistant Professor of Department of Metallurgy and Materials Engineering, Hamedan University of Technology, Hamedan, I. R. Iran https://orcid.org/0000-0002-7379-4265
Keywords: Electrochemical Dissolution, Recovery, Brass Scrap, Predictive Model, GEP

Abstract


Regarding the high corrosion resistance of brass in sulfuric acid, its leaching process is the most important step in hydrometallurgical recovery of brass scraps. In this study, the electrochemical dissolution of brass chips in sulfuric acid has been investigated. The electrochemical cell voltage depends on various parameters. Regarding the complexity of electrochemical dissolution, the system voltage could not be easily predicted based on the operational parameters of the cell. So, it is necessary to use modeling techniques to predict cell voltage. In this study, 139 leaching experiments were conducted under different conditions. Using the experimental results and gene expression programming (GEP), parameters such as acid concentration, current density, temperature and anode-cathode distance were entered as the inputs and the voltage of the electrochemical dissolution was predicted as the output. The results showed that GEP-based model was capable of predicting the voltage of electrochemical dissolution of brass alloy with correlation coefficient of 0.929 and root square mean error (RSME) of 0.052. Based on the sensitivity analysis on the input and output parameters, acid concentration and anode-cathode distance were the most and least effective parameters, respectively. The modeling results confirmed that the proposed model is a powerful tool in designing a mathematical equation between the parameters of electrochemical dissolution and the voltage induced by variation of these parameters.

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Published
2020/09/15
How to Cite
Ghasemi, S., Vaghar, S., Pourzafar, M., Dehghani, H., & Heidarpour, A. (2020). A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: application of gene expression programming. Journal of Mining and Metallurgy, Section B: Metallurgy, 56(2), 237-245. Retrieved from https://aseestant.ceon.rs/index.php/jmm/article/view/23290
Issue
Vol. 56 No. 2 (2020)
Section
Original Scientific Paper

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