Predicting permeate flux and NaCl rejection at nanofiltration process of decolorization columns waste water using artificial neural networks

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Abstract:
In this study, artificial neural network (ANN) model was used to predict the average permeate flux and NaCl rejection in nanofiltration process of sugar industry decolorization columns waste water. Nanofiltration process was performed at three temperatures 30, 4 and 50°C, at three pressures of 1, 1.5 and 2 MPa, at three concentration levels 60, 80 and 100 g/l and at two pH levels 8 and 9. In order to predict the permeate flux and NaCl rejection multi-layer perceptron neural network with 4 inputs and 2 outputs were used. The results showed a network with 9 neurons in hidden layer and using a hyperbolic tangent transfer function and the Levenberg–Marquardt (LM) optimization technique and 30%-30%-40% data for training/ testing/ validating process can be well predict the permeate flux (0.98) and NaCl rejection (0.88) in the nanofiltration of decolorization column wastewater. Sensitivity analysis results by optimum ANN showed the pressure was the most sensitive factor for prediction of both flux and rejection by the selected ANN.
Language:
Persian
Published:
Journal of Food Research (AGRICULTURAL SCIENC), Volume:24 Issue: 1, 2014
Pages:
77 to 87
https://www.magiran.com/p1274321  
سامانه نویسندگان
  • Author (1)
    Fakhreddin Salehi
    Associate Professor Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
    Salehi، Fakhreddin
  • Corresponding Author (2)
    Seyed Mohammad Ali Razavi
    Professor Department of Food Science and Technology, Ferdowsi University, Mashhad, Iran
    Razavi، Seyed Mohammad Ali
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