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 st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
IWA Publishing Journal
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/1/A_comparative_study_of_clonal_selection_algorithm_for.pdf http://irep.iium.edu.my/42776/4/42776_A%20comparative%20study%20of%20clonal%20selection_Scopus.pdf http://irep.iium.edu.my/42776/5/42776_A%20comparative%20study%20of%20clonal%20selection_WOS.pdf |
| Summary: | 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 wellestablished
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, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP. |
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