Recurrent neural network-based control for wastewater treatment process - Details

Author：

Qiao, Junfei (Qiao, Junfei.) (Scholars：乔俊飞) | Huang, Xiaoqi (Huang, Xiaoqi.) | Han, Honggui (Han, Honggui.) (Scholars：韩红桂)

Indexed by：

EI Scopus

Abstract：

Wastewater　treatment　process　(WWTP)　is　difficult　to　be　controlled　because　of　the　complex　dynamic　behavior.　In　this　paper,　a　multi-variable　control　system　based　on　recurrent　neural　network　(RNN)　is　proposed　for　controlling　the　dissolved　oxygen　(DO)　concentration,　nitrate　nitrogen　(S　NO)　concentration　and　mixed　liquor　suspended　solids　(MLSS)　concentration　in　a　WWTP.　The　proposed　RNN　can　be　self-adaptive　to　achieve　control　accuracy,　hence　the　RNN-based　controller　is　applied　to　the　Benchmark　Simulation　Model　No.1　(BSM1)　WWTP　to　maintain　the　DO,　S　NO　and　MLSS　concentrations　in　the　expected　value.　The　simulation　results　show　that　the　proposed　controller　provides　process　control　effectively.　The　performance,　compared　with　PID　and　BP　neural　network,　indicates　that　this　control　strategy　yields　the　most　accurate　for　DO,　S　NO,　and　MLSS　concentrations　and　has　lower　integral　of　the　absolute　error　(IAE),　integral　of　the　square　error　(ISE)　and　mean　square　error　(MSE).　©　2012　Springer-Verlag.

Keyword：

Biochemical oxygen demand Process control Controllers Nitrogen removal Dissolved oxygen Reclamation Recurrent neural networks Wastewater treatment Mean square error Proportional control systems Errors Nitrates

Author Community：

[ 1 ] [Qiao, Junfei]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Huang, Xiaoqi]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 3 ] [Han, Honggui]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China