Title :
A novel ASM2 and SVM compensation method for the effluent quality prediction model of A2O process
Author :
Li Xiaoting ; Pan Feng ; Gao Qi ; Li Weixing ; Lian XiaoFeng
Author_Institution :
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
Abstract :
For the soft measurement of water quality for sewage treatment process, a novel prediction model is proposed to predict the effluent water quality in this paper, which combines the mechanism model with compensation model. Firstly, the ASM2 model is built as the mechanism model to imitate the sewage treatment process, as well as PSO algorithm is used to adjust the kinetic parameters of the ASM2 model. Next, SVM regression is adopted to compensate the prediction error of mechanism model. Finally, the model is tested with real data collected in a sewage treatment plant. The simulation results show that the model can obtain accuracy prediction results and reflect the behavior of sewage treatment efficiently.
Keywords :
effluents; environmental science computing; regression analysis; sewage treatment; sludge treatment; support vector machines; A2O process; ASM2; PSO algorithm; SVM compensation method; SVM regression; activated sludge model; effluent water quality prediction model; kinetic parameters; mechanism model; prediction error compensation; sewage treatment plant; sewage treatment process; water quality soft measurement; Data models; Effluents; Mathematical model; Predictive models; Sewage treatment; Support vector machines; Activated Sludge Model NO.2; Particle Swarm Optimization; Sewage Treatment; Soft Measurement; Support Vector Machine;
Conference_Titel :
Control Conference (ASCC), 2013 9th Asian
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-5767-8
DOI :
10.1109/ASCC.2013.6606382
