Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm

Sie Chun, Ting and Ismail , Amelia Ritahani and A. Malek, Marlinda (2013) Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm. Journal of Environmental Management, 129. pp. 260-265. ISSN 0301-4797

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Abstract

This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak. Keywords: Artificial Immune System; Chemical Oxygen Demand; Prediction; Septic Sludge Treatment Plant; Total Suspended Solids

Item Type:	Article (Journal)
Additional Information:	4296/31627
Uncontrolled Keywords:	Artificial immune system;Chemical oxygen demand;Prediction;Septic sludge treatment plant; Total suspended solids
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) > TA170 Environmental engineering. Sustainable engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	30 Aug 2013 09:19
Last Modified:	17 Apr 2014 08:27
URI:	http://irep.iium.edu.my/id/eprint/31627

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