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

• Main Authors: Ting S.C., Ismail A.R., Malek M.A.
• Other Authors: 56338030500
• Format: Article
• Published: Academic Press 2023
• Subjects: Artificial immune system; Chemical oxygen demand; Prediction; Septic sludge treatment plant; Total suspended solids; Algorithms; Biological Oxygen Demand Analysis; Bioreactors; Borneo; Environmental Monitoring; Malaysia; Models, Theoretical; Particulate Matter; Sewage; Waste Disposal, Fluid; Water Pollutants, Chemical; East Malaysia; Sarawak; Batch reactors; Effluent treatment; Forecasting; Immune system; Mean square error; Oxygen; Wastewater treatment; Artificial Immune System; Chemical-oxygen demands; Clonal selection algorithms; Prediction modelling; Reactor technology; Sequence batch reactors; Sludge treatment plants; algorithm; bioreactor; chemical oxygen demand; effluent; immune system; pollutant removal; sewage treatment; sludge; suspended load; article; clonal selection algorithm; forecasting; pattern recognition; plant model; prediction; septic sludge treatment plant; septic tank; sequencing batch reactor; sewage treatment plant; sludge dewatering; sludge treatment; suspended particulate matter; waste water; Effluents

This 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.