Modeling a multivariable reactor and on-line model predictive control

• Published in: ISA transactions Vol. 44; no. 4; pp. 539 - 559
• Main Authors: Yu, D.W., Yu, D.L.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2005; Elsevier
• Subjects: Algorithms; Applied sciences; Bioreactors; Chemical Industry - instrumentation; Chemical Industry - methods; Chemical reactor; Computer science; control theory; systems; Computer Simulation; Computer Systems; Control theory. Systems; Equipment Design - methods; Equipment Failure Analysis - methods; Exact sciences and technology; Feedback; first principle models; General equipment and techniques; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Models, Theoretical; MPC; Online Systems; Physics; Servo and control equipment; robots; Systems Theory; MPC; first principle models; Chemical reactor; pH value; Multivariable systems; Biochemistry; Extended Kalman filter; Optimization; Model predictive control; Multistep method; Decentralized control; Non linear model; Discrete time; Variable parameter; Modelling; Chemical reactors; Differential integral proportional control
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/S0019-0578(07)60059-7

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.