Modeling and Optimization of Acid Blue 193 Removal by UV and Peroxydisulfate Process
Publication: Journal of Environmental Engineering
Volume 144, Issue 8
Abstract
In this study, the advanced oxidation process for dye removal from textile wastewater treatment was investigated by means of an experimental setup in which the effect of several parameters on dye removal efficiency [peroxydisulfate concentration, ultraviolet (UV) irradiation, temperature, dye concentration, and time] was examined. In order to predict the removal efficiency, two types of artificial neural networks were used: an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network determined with differential evolution called hybrid self-adaptive differential evolution with neural networks (hSADE-NN). After the successful development of ANFIS, its ability to predict test data was checked. Also, a series of models of the process was determined with hSADE-NN. Comparison of the two approaches indicates that both methods provide good results, the average absolute relative error for hSADE-NN being 3.61% and that for ANFIS 5.18%. After that, a process optimization was performed, the scope being to determine the conditions for maximum dye removal efficiency under various constraints, considered as a means to reduce resources consumed.
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Information
Published In
Journal of Environmental Engineering
Volume 144 • Issue 8 • August 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Feb 11, 2017
Accepted: Feb 13, 2018
Published online: May 25, 2018
Published in print: Aug 1, 2018
Discussion open until: Oct 25, 2018
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