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

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dc.citedby1
dc.contributor.authorChun T.S.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorIsmail A.R.en_US
dc.contributor.authorid56338030500en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid36995749000en_US
dc.date.accessioned2023-05-29T06:02:01Z
dc.date.available2023-05-29T06:02:01Z
dc.date.issued2015
dc.descriptionAlgorithms; 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, Fluiden_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/wst.2014.451
dc.identifier.epage528
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84925263557
dc.identifier.spage524
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84925263557&doi=10.2166%2fwst.2014.451&partnerID=40&md5=a1cbc7f3ad9759d6528362355fa77412
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22561
dc.identifier.volume71
dc.publisherIWA Publishingen_US
dc.sourceScopus
dc.sourcetitleWater Science and Technology
dc.titleA comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment planten_US
dc.typeArticleen_US
dspace.entity.typePublication
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