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Membrane Water Treatment
  Volume 9, Number 4, July 2018 , pages 285-292

Application of ANN modeling for oily wastewater treatment by hybrid PAC-MF process
Mohsen Abbasi, Yaser Rasouli and Peyman Jowkar

    In the following study, Artificial Neural Network (ANN) is used for prediction of permeate flux decline during oily wastewater treatment by hybrid powdered activated carbon-microfiltration (PAC-MF) process using mullite and mullite-alumina ceramic membranes. Permeate flux is predicted as a function of time and PAC concentration. To optimize the networks performance, different transfer functions and different initial weights and biases have been tested. Totally, more than 850,000 different networks are tested for both membranes. The results showed that 10:6 and 9:20 neural networks work best for mullite and mullite-alumina ceramic membranes in PAC-MF process, respectively. These networks provide low mean squared error and high linearity between target and predicted data (high R2 value). Finally, the results present that ANN provide best results (R2 value equal to 0.99999) for prediction of permeation flux decline during oily wastewater treatment in PAC-MF process by ceramic membranes.
Key Words
    artificial neural network; ceramic membranes; oily wastewater treatment; microfiltration; powdered activated carbon
Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Persian Gulf University, Bushehr,75169, Iran

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