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      저자 : Behrooz Mirza (검색결과 1 건)
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        Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach

        Ramin Badrnezhad,Behrooz Mirza 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.2

        Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) ofoily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimizeoperating parameters involved in the process model result in attaining desired permeate flux, is offundamental great interest from an economical and technical point of view. Accordingly, this currentresearch proposed a hybrid process modeling and optimization based on computational intelligenceparadigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meetsthe challenge of specified-objective based on two steps: first the development of bio-inspired approachbased on ANN, trained, validated and tested successfully with experimental data collected during thepolyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale inwhich the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flowvelocity (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 applyingGA, with a view to realizing maximum or minimum process output variable. The results obtainedvalidate the estimates of the ANN–GA technique with a good accuracy. Finally, the relative importance ofthe controllable operation factors on flux decline is determined by applying the various correlationstatistic techniques. According to the result of the sensitivity analysis based on the correlationcoefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.

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