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

Algorithms; Artificial intelligence; Biochemical oxygen demand; Bioinformatics; Developing countries; Effluent treatment; Effluents; Forecasting; Least squares approximations; Oxygen; Pattern recognition; Support vector machines; Water quality; Biological oxygen demand; Clonal selection algorithms;...

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Main Authors: Chun T.S., Malek M.A., Ismail A.R.
Other Authors: 56338030500
Format: Article
Published: IWA Publishing 2023
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spelling my.uniten.dspace-225612023-05-29T14:02:02Z A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant Chun T.S. Malek M.A. Ismail A.R. 56338030500 55636320055 36995749000 Algorithms; Artificial intelligence; Biochemical oxygen demand; Bioinformatics; Developing countries; Effluent treatment; Effluents; Forecasting; Least squares approximations; Oxygen; Pattern recognition; Support vector machines; Water quality; Biological oxygen demand; Clonal selection algorithms; Least-square support vector machines; Sludge treatment plants; Total suspended solids; Chemical oxygen demand; oxygen; sewage; algorithm; clone; comparative study; effluent; least squares method; nonlinearity; pattern recognition; simulation; sludge; water treatment; activated sludge; algorithm; Article; biochemical oxygen demand; chemical oxygen demand; clonal selection algorithm; comparative study; computer simulation; effluent; forecasting; pattern recognition; prediction; regression analysis; septic sludge treatment plant; sludge treatment; statistical model; support vector machine; suspended particulate matter; waste water treatment plant; chemistry; procedures; sewage; theoretical model; Algorithms; Biological Oxygen Demand Analysis; Forecasting; Least-Squares Analysis; Models, Theoretical; Sewage; Support Vector Machines; Waste Disposal, Fluid 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, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP. � IWA Publishing 2015. Final 2023-05-29T06:02:01Z 2023-05-29T06:02:01Z 2015 Article 10.2166/wst.2014.451 2-s2.0-84925263557 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925263557&doi=10.2166%2fwst.2014.451&partnerID=40&md5=a1cbc7f3ad9759d6528362355fa77412 https://irepository.uniten.edu.my/handle/123456789/22561 71 4 524 528 IWA Publishing Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Algorithms; Artificial intelligence; Biochemical oxygen demand; Bioinformatics; Developing countries; Effluent treatment; Effluents; Forecasting; Least squares approximations; Oxygen; Pattern recognition; Support vector machines; Water quality; Biological oxygen demand; Clonal selection algorithms; Least-square support vector machines; Sludge treatment plants; Total suspended solids; Chemical oxygen demand; oxygen; sewage; algorithm; clone; comparative study; effluent; least squares method; nonlinearity; pattern recognition; simulation; sludge; water treatment; activated sludge; algorithm; Article; biochemical oxygen demand; chemical oxygen demand; clonal selection algorithm; comparative study; computer simulation; effluent; forecasting; pattern recognition; prediction; regression analysis; septic sludge treatment plant; sludge treatment; statistical model; support vector machine; suspended particulate matter; waste water treatment plant; chemistry; procedures; sewage; theoretical model; Algorithms; Biological Oxygen Demand Analysis; Forecasting; Least-Squares Analysis; Models, Theoretical; Sewage; Support Vector Machines; Waste Disposal, Fluid
author2 56338030500
author_facet 56338030500
Chun T.S.
Malek M.A.
Ismail A.R.
format Article
author Chun T.S.
Malek M.A.
Ismail A.R.
spellingShingle Chun T.S.
Malek M.A.
Ismail A.R.
A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
author_sort Chun T.S.
title A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_short A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_full A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_fullStr A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_full_unstemmed A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_sort comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
publisher IWA Publishing
publishDate 2023
_version_ 1806428098686615552
score 13.211869

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