APPLYING THE GENETIC ALGORITHM–ARTIFICIAL NEURAL NETWORK MODELING FOR PREDICTION OF PERMEATE FLUX, NACL REJECTION AND TOTAL HYDRAULIC RESISTANCE OF NANO FILTRATION PROCESS

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SALEHI F.; RAZAVI S.M.A.

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Journal: JOURNAL OF SEPARATION AND TRANSPORT PHENOMENA (JOURNAL OF SCHOOL OF ENGINEERING) Year:2011 | Volume:22 | Issue:1 Page(s): 29-50

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Title

APPLYING THE GENETIC ALGORITHM–ARTIFICIAL NEURAL NETWORK MODELING FOR PREDICTION OF PERMEATE FLUX, NACL REJECTION AND TOTAL HYDRAULIC RESISTANCE OF NANO FILTRATION PROCESS

Author(s)

SALEHI F. | RAZAVI S.M.A. | Issue Writer Certificate

Keywords

GENETIC ALGORITHMQ2

FLUXQ3

REJECTIONQ2

TOTAL HYDRAULIC RESISTANCEQ3

MODELINGQ2

Abstract

In this study, the effect of operating parameters (temperature and pressure) and feed properties (feed concentration and pH) on performance of Nano filtration process (permeate FLUX, NaCl REJECTION and TOTAL HYDRAULIC RESISTANCE) was investigated and GENETIC ALGORITHM–artificial neural network (GA-ANN) method was used to model these parameters during the Nano filtration of wastewater obtained from ion-exchange resins regeneration in decolorizing columns of sugar industry. The polyamide tubular AFC80 membrane provided by PCI Company was used for experiments. In order to predict the permeate FLUX, NaCl REJECTION and TOTAL HYDRAULIC RESISTANCE, multi-layer perceptron neural network with 4 inputs and 3 outputs was used. GENETIC ALGORITHM method was used to optimize the number of neurons in ANN hidden layer. The results showed that the TOTAL HYDRAULIC RESISTANCE increases with increase in trans membrane pressure and feed concentration. The permeate FLUX increases with increasing the temperature and pH, whereas the TOTAL HYDRAULIC RESISTANCE declines. Average permeate FLUX was found to be 7.7±3.7 kg/m2h. The REJECTION of sodium chloride was between 16% and 42.7%. The results of GAANN MODELING method showed that the combination of ANN and GA gives better results and by combining these methods, analysis rate and precision of MODELING increases. By using a network with 24 neurons in one hidden layer, the sigmoid transfer function and 30%/20%/50% of data for training/testing/validating process, the permeate FLUX (0.98), NaCl REJECTION (0.94) and TOTAL HYDRAULIC RESISTANCE (0.96) can be well predicted in the Nano filtration of decolorization column wastewater.

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APA: Copy

SALEHI, F., & RAZAVI, S.M.A.. (2011). APPLYING THE GENETIC ALGORITHM–ARTIFICIAL NEURAL NETWORK MODELING FOR PREDICTION OF PERMEATE FLUX, NACL REJECTION AND TOTAL HYDRAULIC RESISTANCE OF NANO FILTRATION PROCESS. JOURNAL OF SEPARATION AND TRANSPORT PHENOMENA (JOURNAL OF SCHOOL OF ENGINEERING), 22(1), 29-50. SID. https://sid.ir/paper/196177/en

Vancouver: Copy

SALEHI F., RAZAVI S.M.A.. APPLYING THE GENETIC ALGORITHM–ARTIFICIAL NEURAL NETWORK MODELING FOR PREDICTION OF PERMEATE FLUX, NACL REJECTION AND TOTAL HYDRAULIC RESISTANCE OF NANO FILTRATION PROCESS. JOURNAL OF SEPARATION AND TRANSPORT PHENOMENA (JOURNAL OF SCHOOL OF ENGINEERING)[Internet]. 2011;22(1):29-50. Available from: https://sid.ir/paper/196177/en

IEEE: Copy

F. SALEHI, and S.M.A. RAZAVI, “APPLYING THE GENETIC ALGORITHM–ARTIFICIAL NEURAL NETWORK MODELING FOR PREDICTION OF PERMEATE FLUX, NACL REJECTION AND TOTAL HYDRAULIC RESISTANCE OF NANO FILTRATION PROCESS,” JOURNAL OF SEPARATION AND TRANSPORT PHENOMENA (JOURNAL OF SCHOOL OF ENGINEERING), vol. 22, no. 1, pp. 29–50, 2011, [Online]. Available: https://sid.ir/paper/196177/en

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