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Removal of Cu(II) ions from aqueous solutions by an ion‐exchange process: Modeling and optimization
Author(s) -
Bleotu Irina,
Dragoi Eleiculina,
Mureşeanu Mihaela,
Dorneanu SorinAurel
Publication year - 2018
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.12793
Subject(s) - aqueous solution , copper , ion exchange , mean squared error , artificial neural network , process (computing) , iminodiacetic acid , ion , maximization , chemistry , materials science , computer science , inorganic chemistry , mathematics , metallurgy , mathematical optimization , chelation , statistics , organic chemistry , machine learning , operating system
In this paper, an optimal artificial neural network determined by a self‐adaptive differential evolution approach is applied to model and optimize the removal of copper from wastewater by an ion‐exchange process. The Purolite S930 + resin with iminodiacetic group was used in batch mode for Cu(II) removal from synthetic aqueous solutions in different working conditions (initial solution pH, stirring rate, initial concentration of copper, temperature, contact time and resin amount). The obtained results indicated that the used methodology was able to provide good models for the studied process, the mean squared error in the testing phase obtained by the best network being 0.0034. In addition, the optimal combination of parameters leading to the maximization of removal efficiency determined with the proposed approach was experimentally validated, the prediction being in correlation with the observed data. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 605–612, 2018