Conference paper
Simulation of NMPC for a Laboratory Adiabatic CSTR with an Exothermic Reaction
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
CHEC Research Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark3
Department of Chemical and Biochemical Engineering, Technical University of Denmark4
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark5
PROSYS - Process and Systems Engineering Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark6
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark7
In this paper, we present nonlinear system identification and nonlinear model predictive control (NMPC) for a laboratory-scale adiabatic continuous stirred tank reactor (CSTR) with an exothermic reaction. We describe the equipment used in the process, and we present a process model based on first principles.
We use a maximum likelihood estimation (MLE) approach based on the process model and the continuous-discrete extended Kalman filter (CD-EKF) to estimate four model parameters. The NMPC is based on the process model (with the estimated model parameters), the CD-EKF, and a nonlinear least-squares regulator with input (Tikhonov) and rate-of-movement regularization.
We present simulations demonstrating that the NMPC (implemented in Python and C) can track any stable and unstable steady state for this system with multiple steady states in some operational regions.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2020 |
| Pages: | 202-207 |
| Proceedings: | 2020 European Control ConferenceEuropean Control Conference |
| ISBN: | 172818813X , 172818813x , 3907144023 , 9781728188133 and 9783907144022 |
| Types: | Conference paper |
| DOI: | 10.23919/ECC51009.2020.9143733 |
| ORCIDs: | Jørgensen, John Bagterp , Ritschel, Tobias Kasper Skovborg , Boiroux, Dimitri , Wahlgreen, Morten Ryberg , Wu, Hao and Huusom, Jakob Kjøbsted |
Adiabatic C language CD-EKF Chemical reactors Heuristic algorithms Kalman filters MLE Mathematical model Maximum likelihood estimation NMPC simulation Parameter estimation Python Temperature measurement chemical reactors chemical technology continuous-discrete extended Kalman filter exothermic reaction identification laboratory adiabatic CSTR laboratory-scale adiabatic continuous stirred tank reactor maximum likelihood estimation nonlinear control systems nonlinear filters nonlinear model predictive control nonlinear system identification parameter estimation predictive control rate-of-movement regularization thermodynamics
