A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming
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...
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| Format: | Article |
| Language: | English |
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University of Belgrade, Technical Faculty, Bor
2020-01-01
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| Series: | Journal of Mining and Metallurgy. Section B: Metallurgy |
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| Online Access: | http://www.doiserbia.nb.rs/img/doi/1450-5339/2020/1450-53392000012G.pdf |
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| author | Ghasemi S. Vaghar S. Pourzafar M. Dehghani H. Heidarpour A. |
| author_facet | Ghasemi S. Vaghar S. Pourzafar M. Dehghani H. Heidarpour A. |
| author_sort | Ghasemi S. |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5d626991a3c844929f640bf1dd2a67f5 |
| institution | Kabale University |
| issn | 1450-5339 2217-7175 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | University of Belgrade, Technical Faculty, Bor |
| record_format | Article |
| series | Journal of Mining and Metallurgy. Section B: Metallurgy |
| spelling | doaj-art-5d626991a3c844929f640bf1dd2a67f52025-02-02T15:54:01ZengUniversity of Belgrade, Technical Faculty, BorJournal of Mining and Metallurgy. Section B: Metallurgy1450-53392217-71752020-01-0156223724510.2298/JMMB190924012G1450-53392000012GA novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programmingGhasemi S.0Vaghar S.1Pourzafar M.2Dehghani H.3Heidarpour A.4Hamedan University of Technology, Department of Metallurgy and Materials Engineering, Hamedan, IranHamedan University of Technology, Department of Metallurgy and Materials Engineering, Hamedan, IranDepartment of Mining Engineering, Hamedan University of Technology, Hamedan, IranHamedan University of Technology, Hamedan, IranHamedan University of Technology, Department of Metallurgy and Materials Engineering, Hamedan, IranRegarding 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.http://www.doiserbia.nb.rs/img/doi/1450-5339/2020/1450-53392000012G.pdfelectrochemical dissolutionrecoverybrass scrappredictive modelgep |
| spellingShingle | Ghasemi S. Vaghar S. Pourzafar M. Dehghani H. Heidarpour A. 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 electrochemical dissolution recovery brass scrap predictive model gep |
| title | A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming |
| title_full | A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming |
| title_fullStr | A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming |
| title_full_unstemmed | A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming |
| title_short | A novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass: Application of gene expression programming |
| title_sort | novel predictive model for estimation of cell voltage in electrochemical recovery of copper from brass application of gene expression programming |
| topic | electrochemical dissolution recovery brass scrap predictive model gep |
| url | http://www.doiserbia.nb.rs/img/doi/1450-5339/2020/1450-53392000012G.pdf |
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