화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.20, No.2, 528-543, March, 2014
DOI10.1016/j.jiec.2013.05.012  Export Citation
Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach
Ramin Badrnezhad, and Behrooz Mirza1, †
  • Faculty of Chemical Engineering, Amirkabir University of Technology, Hafez Ave., Tehran, Iran
  • 1Department of Chemistry, Faculty of Science, South Tehran Branch, Islamic Azad University, Tehran, Iran
E-mail:
Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) of oily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimize operating parameters involved in the process model result in attaining desired permeate flux, is of fundamental great interest from an economical and technical point of view. Accordingly, this current research proposed a hybrid process modeling and optimization based on computational intelligence paradigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meets the challenge of specified-objective based on two steps: first the development of bio-inspired approach based on ANN, trained, validated and tested successfully with experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale in which the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and filtration time as inputs; and gave permeate flux as an output. Subsequently, the 5-dimensional input space of the ANN model portraying process input variables was optimized by applying GA, with a view to realizing maximum or minimum process output variable. The results obtained validate the estimates of the ANN.GA technique with a good accuracy. Finally, the relative importance of the controllable operation factors on flux decline is determined by applying the various correlation statistic techniques. According to the result of the sensitivity analysis based on the correlation coefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.
Keywords:Artificial neural network;Genetic algorithm;Industrial oily wastewater;Sensitivity analysis;Ultrafiltration
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