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Application of model predictive control to the BSM1 benchmark of wastewater treatment process
- Source :
- Computers & Chemical Engineering, Computers & Chemical Engineering, Elsevier, 2008, 32 (12), pp.2849-2856. ⟨10.1016/j.compchemeng.2008.01.009⟩
- Publication Year :
- 2008
- Publisher :
- Elsevier BV, 2008.
-
Abstract
- International audience; Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results showthat MPC can be effectively used for control inwastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.
- Subjects :
- 0209 industrial biotechnology
Engineering
Model predictive control Wastewater treatment process BSM1 benchmark
business.industry
General Chemical Engineering
Feed forward
02 engineering and technology
Energy consumption
6. Clean water
Computer Science Applications
Model predictive control
Nonlinear system
020901 industrial engineering & automation
020401 chemical engineering
Control theory
Benchmark (computing)
[SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering
Penalty method
Sewage treatment
0204 chemical engineering
business
Effluent
Subjects
Details
- ISSN :
- 00981354 and 18734375
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Computers & Chemical Engineering
- Accession number :
- edsair.doi.dedup.....01299419ffac853f8249116ccbf5d871