A novel ASM2 and SVM compensation method for the effluent quality prediction model of A2O process

Xiaoting Li, Pan Feng, Gao Qi, Weixing Li, Xiaofeng Lian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2013 9th Asian Control Conference, ASCC 2013
DOIs
Publication statusPublished - 2013
Event2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey
Duration: 23 Jun 201326 Jun 2013

Publication series

Name2013 9th Asian Control Conference, ASCC 2013

Conference

Conference2013 9th Asian Control Conference, ASCC 2013
Country/TerritoryTurkey
CityIstanbul
Period23/06/1326/06/13

Keywords

  • Activated Sludge Model NO.2
  • Particle Swarm Optimization
  • Sewage Treatment
  • Soft Measurement
  • Support Vector Machine

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