alexa Treatment of Textile Wastewater by Nanofiltration Membranes: A Neural Network Approach
ISSN: 2165-8064

Journal of Textile Science & Engineering
Open Access

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Research Article

Treatment of Textile Wastewater by Nanofiltration Membranes: A Neural Network Approach

Jahangiri M and Aminian A*

School of chemical, petroleum and gas engineering, Semnan University, Semnan, Iran

*Corresponding Author:
Aminian A
School of chemical, petroleum and gas engineering
Semnan University, Semnan, Iran
Tel: +98 9124319323
Fax: +98 2313354089
E-mail: [email protected]

Received Date: July 30, 2012; Accepted Date: September 24, 2012; Published Date: October 01, 2012

Citation: Jahangiri M, Aminian A (2012) Treatment of Textile Wastewater by Nanofiltration Membranes: A Neural Network Approach. J Textile Sci Eng 2:119. doi: 10.4172/2165-8064.1000119

Copyright: © 2012 Jahangiri M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Textile industries represent an important environmental problem due to their high water consumption. In order to economically water consumption, wastewater treatment is necessary for water reuse in the textile industries. Predicting the performance of nanofiltration membrane, as an effective separation process, is necessary for the design and depiction of process. Prediction of the rejection of degradable components is especially important. In this work, an Artificial Neural Network (ANN) is used to predict the rejection of Chemical Oxygen Demand (COD) in a cross-flow nanofiltration membrane at textile wastewater effluent stream. Rejections are predicted as a function of feed pressure and permeate flux with cross flow velocity. ANN predictions of the COD rejection are compared with experimental results obtained using two different nanofiltration membranes (NF-90 and DK-5). The results show a good agreement between experimental data and the output from the neural network simulation.


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