Title: Development of an adaptive non-parametric model for estimating maximum efficiency of disc membrane

Authors: Anirban Banik; Sushant Kumar Biswal; Mrinmoy Majumder; Tarun Kanti Bandyopadhyay

Addresses: Department of Civil Engineering, National Institute of Technology, Agartala Jirania, Agartala, Tripura,799046, India ' Department of Civil Engineering, National Institute of Technology, Agartala Jirania, Agartala, Tripura,799046, India ' Department of Civil Engineering, National Institute of Technology, Agartala Jirania, Agartala, Tripura,799046, India ' Department of Chemical Engineering, National Institute of Technology, Agartala Jirania, Agartala, Tripura, 799046, India

Abstract: Membrane separation and filtration process are a technique of removing the impurities from the feed stream based on the pore size of the membrane bed. Permeate stream produced by the membrane are good quality due to this membrane found wide application in the field of water purification, gas-gas separation, etc. In the concerned study, the ability of the cellulose acetate disc membrane for improving the quality of the rubber industrial effluent of Tripura has been investigated in pilot scale. GMDH-multilayered feedback algorithm has been implemented to predict the maximum efficiency of the membrane. The efficiency of the membrane is maximised for the optimal value of pore size, inlet velocity, and operating pressure. It has been found that efficiency of the membrane is maximised when the pore size of the membrane is kept 2.060538 µm, inlet velocity is 0.201896 m/sec and operating pressure is 694.7201 kPa. The performance of the prepared GMDH model is evaluated by using model evaluation technique like NSE, PBIAS, slope and Y-intercept, RSR. It has been found that software predicted data can be used for trouble shooting and optimal design of the membrane bed.

Keywords: membrane; GMDH; neural network; membrane separation technique; optimisation; convergence computing.

DOI: 10.1504/IJCONVC.2018.091111

International Journal of Convergence Computing, 2018 Vol.3 No.1, pp.3 - 19

Received: 03 May 2017
Accepted: 10 Aug 2017
Published online: 10 Apr 2018 *

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