A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant.
The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
Department of Civil Engineering, Universiti Tenaga Nasional, IKRAM-UNITEN Road, Kajang, Selangor 43000, Malaysia E-mail: sie_chun@hotmail.com.
Ting Sie
TS
Chun
Institute of Energy, Policy and Research (IEPRE), Universiti Tenaga Nasional, IKRAM-UNITEN Road, Kajang, Selangor 43000, Malaysia.
M A
MA
Malek
Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, P.O. Box 10, Kuala Lumpur 50728, Malaysia.
Amelia Ritahani
AR
Ismail
D000465:Algorithms; D057919:Biological Oxygen Demand Analysis; D005544:Forecasting; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D012722:Sewage / Q000737:chemistry*; D060388:Support Vector Machine; D014865:Waste Disposal, Fluid / Q000379:methods*
D012722:Sewage