Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm

CATEGORY: MEMBRANES
Chemical Engineering Research and Design, Available online 22 August 2012, In Press, Corrected Proof
Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm
Reza Soleimani a, Navid Alavi Shoushtari b, Behrooz Mirza c, Abdolhamid Salahi d
a Department of Gas & Chemical Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
b Department of Chemical & Petroleum Engineering, Sharif University of Technology, Tehran, Iran
c Department of Chemistry, Faculty of Science, South Tehran Branch, Islamic Azad University, Tehran, Iran
d Department of Applied Chemistry, Faculty of Science, East Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
Describes a study of oily wastewater treatment with commercial polyacrylonitrile (PAN) ultrafiltration (UF) membranes.  The outlet wastewater of the API (American Petroleum Institute) unit of a Tehran refinery is used as the feed.  
The study purpose was to predict the permeation flux and fouling resistance by applying artificial neural networks (ANNs), and then optimizing the operating conditions in separation of oil from industrial oily wastewaters, including trans-membrane pressure (TMP), cross-flow velocity (CFV), feed temperature and pH, so that a maximum permeation flux accompanied by a minimum fouling resistance, was acquired by applying genetic algorithm as a powerful soft computing technique.
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