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Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance

Jami, Mohammed Saedi and Husain, Iman A. F. and Kabbashi, Nassereldeen Ahmed and Abdullah, Norhafiza (2012) Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance. Australian Journal of Basic and Applied Sciences, 6 (1). pp. 62-69. ISSN 1991-8178

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Abstract

It is difficult to unveil the complicated interrelationships of wastewater parameters using linear models. A statistical modeling tool called artificial neural network (ANN) is used in this work to predict the performance of wastewater treatment plant (WWTP). Extensive influent and effluent parameters database containing measured data spanning over two years of period was used to develop and train ANN using ANN toolbox in commercially available software, MATLAB. The data were obtained from one of Sewage Treatment Plant in Malaysia. The input parameters for the ANN were BOD, SS, and COD of the influent, while the output parameters were combination of the effluent characteristics. The networks for single input-single output were compared with those of single inputmultiple output. The ANN was developed for raw and screened data and the results were compared for both networks. It was found that the use of data screening is essential to come up with a better ANNs model. From the regression analysis, networks with one hidden layer and 20 neurons were found to be the best one for single input-single output approach. While the best network for the multiple inputssingle output approach was with BOD as outputs and 30 neurons. The multiple inputs- single output ANN models developed can be used in analyzing how wastewater parameters such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Suspended Solids (SS) are affecting each other.

Item Type: Article (Journal)
Additional Information: 5545/17701
Uncontrolled Keywords: Artificial neural network, Wastewater treatment, Regression analysis
Subjects: T Technology > TP Chemical technology > TP155 Chemical engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Biotechnology Engineering
Depositing User: Professor Dr. Mohammed Saedi Jami, PhD CEng MIChemE
Date Deposited: 15 Jun 2012 09:37
Last Modified: 15 Jun 2012 09:37
URI: http://irep.iium.edu.my/id/eprint/17701

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