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Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm

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dc.citedby6
dc.contributor.authorTing S.C.en_US
dc.contributor.authorIsmail A.R.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid56338030500en_US
dc.contributor.authorid36995749000en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-12-29T07:43:42Z
dc.date.available2023-12-29T07:43:42Z
dc.date.issued2013
dc.description.abstractThis study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak. � 2013 Elsevier Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.jenvman.2013.07.022
dc.identifier.epage265
dc.identifier.scopus2-s2.0-84882990494
dc.identifier.spage260
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84882990494&doi=10.1016%2fj.jenvman.2013.07.022&partnerID=40&md5=95af3b80df52fd3c75f02bd65063cf91
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29946
dc.identifier.volume129
dc.pagecount5
dc.publisherAcademic Pressen_US
dc.sourceScopus
dc.sourcetitleJournal of Environmental Management
dc.subjectArtificial immune system
dc.subjectChemical oxygen demand
dc.subjectPrediction
dc.subjectSeptic sludge treatment plant
dc.subjectTotal suspended solids
dc.subjectAlgorithms
dc.subjectBiological Oxygen Demand Analysis
dc.subjectBioreactors
dc.subjectBorneo
dc.subjectEnvironmental Monitoring
dc.subjectMalaysia
dc.subjectModels, Theoretical
dc.subjectParticulate Matter
dc.subjectSewage
dc.subjectWaste Disposal, Fluid
dc.subjectWater Pollutants, Chemical
dc.subjectBorneo
dc.subjectEast Malaysia
dc.subjectMalaysia
dc.subjectSarawak
dc.subjectBatch reactors
dc.subjectChemical oxygen demand
dc.subjectEffluent treatment
dc.subjectForecasting
dc.subjectImmune system
dc.subjectMean square error
dc.subjectOxygen
dc.subjectWastewater treatment
dc.subjectArtificial immune system
dc.subjectChemical oxygen demand
dc.subjectPrediction
dc.subjectSeptic sludge treatment plant
dc.subjectTotal suspended solids
dc.subjectArtificial Immune System
dc.subjectChemical-oxygen demands
dc.subjectClonal selection algorithms
dc.subjectPrediction modelling
dc.subjectReactor technology
dc.subjectSarawak
dc.subjectSeptic sludge treatment plant
dc.subjectSequence batch reactors
dc.subjectSludge treatment plants
dc.subjectTotal suspended solids
dc.subjectalgorithm
dc.subjectbioreactor
dc.subjectchemical oxygen demand
dc.subjecteffluent
dc.subjectimmune system
dc.subjectpollutant removal
dc.subjectsewage treatment
dc.subjectsludge
dc.subjectsuspended load
dc.subjectalgorithm
dc.subjectarticle
dc.subjectBorneo
dc.subjectchemical oxygen demand
dc.subjectclonal selection algorithm
dc.subjecteffluent
dc.subjectforecasting
dc.subjectpattern recognition
dc.subjectplant model
dc.subjectprediction
dc.subjectseptic sludge treatment plant
dc.subjectseptic tank
dc.subjectsequencing batch reactor
dc.subjectsewage treatment plant
dc.subjectsludge dewatering
dc.subjectsludge treatment
dc.subjectsuspended particulate matter
dc.subjectwaste water
dc.subjectEffluents
dc.titleDevelopment of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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