Universiti Teknologi Malaysia Institutional Repository

Fouling prediction using neural network model for membrane bioreactor system

Mahmod, N. and Wahab, N. A. (2017) Fouling prediction using neural network model for membrane bioreactor system. Indonesian Journal of Electrical Engineering and Computer Science, 6 (1). pp. 200-206. ISSN 2502-4752

[img]
Preview
PDF
583kB

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Membrane bioreactor (MBR) technology is a new method for water and wastewater treatment due to its ability to produce better and high-quality effluent that meets water quality regulations. MBR also is an advanced way to displace the conventional activated sludge (CAS) process. Even this membrane gives better performances compared to CAS, it does have few drawbacks such as high maintenance cost and fouling problem. In order to overcome this problem, an optimal MBR plant operation needs to be developed. This can be achieved through an accurate model that can predict the fouling behaviour which could optimise the membrane operation. This paper presents the application of artificial neural network technique to predict the filtration of membrane bioreactor system. The Radial Basis Function Neural Network (RBFNN) is applied to model the developed submerged MBR filtration system. RBFNN model is expected to give good prediction model of filtration system for estimating the fouling that formed during filtration process.

Item Type:Article
Uncontrolled Keywords:Artificial neural network, Fouling, Membrane bioreactor, Radial basis function
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:74883
Deposited By: Widya Wahid
Deposited On:13 Mar 2018 18:06
Last Modified:13 Mar 2018 18:06

Repository Staff Only: item control page