Cu-Zn separation by supported liquid membrane analyzed through multi-objective genetic algorithms

A1 Journal article (refereed)

Internal Authors/Editors

Publication Details

List of Authors: Debanga Nandan Mondal, Kadambini Sarangi, Frank Pettersson, Prodip Kumar Sen, Henrik Saxén, Nirupam Chakraborti
Publication year: 2011
Journal: Hydrometallurgy
Journal acronym: HYDROMETALLURGY
Volume number: 107
Issue number: 3-4
Start page: 112
End page: 123
Number of pages: 12
ISSN: 0304-386X
eISSN: 1879-1158


Data driven models were constructed for the Cu-Zn separation process using Di (2-ethyl hexyl) phosphoric acid (D2EHPA) as the mobile carrier in a supported liquid membrane. The modeling strategy involved using an Evolutionary Neural Network that used Multi-objective Genetic Algorithms to configure its weights and topology. The model predictions served as the objectives for subsequent bi-objective optimization tasks involving (i) maximization of Zn, along with minimization of Cu and also (ii) maximization of Cu, along with minimization of Zn, all in the strip side after some fixed periods of extraction. The analyses of the results led to the most suitable conditions for optimum separation of Cu and Zn. A Multi-objective Genetic Algorithm was used for the optimization task. Similar analyses were also performed using the commercial software modeFRONTIER (TM) and the results were compared and contrasted.


D2EHPA, Evolutionary algorithm, Extraction, Genetic algorithms, modeFRONTIER (TM), Multi-objective optimization, Neural network, Optimization, Separation

Last updated on 2019-19-11 at 04:29