A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

Water Science and Technology : a Journal of the International Association on Water Pollution Research
Ting Sie ChunAmelia Ritahani Ismail

Abstract

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.

References

Mar 29, 2006·IEEE Transactions on Neural Networks·Hongbin DuPingjing Yao
Sep 21, 2007·Water Science and Technology : a Journal of the International Association on Water Pollution Research·H Orth
Mar 24, 2010·Journal of Hazardous Materials·Emad S ElmollaMohamed Meselhy Eltoukhy
Feb 9, 2012·Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·Yang YangXiaoyu Xu
Feb 10, 2012·Free Radical Research·Ting-Ting Huang
Jul 10, 2014·Environmental Science. Processes & Impacts·Ting Sie ChunAmelia Ritahani Ismail

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