Fuzzy Model-based Predictive Control of a Chemical Reactor
Systematic methods and tools for managing the complexity
Process Control (T4-8P)
Keywords: chemical reactor, fuzzy identification, predictive control
Model-based predictive control (MBPC) refers to a class of control algorithms, which are based on a process model. MBPC can be applied to such systems as e.g. multivariable, non-minimum-phase, open-loop unstable, non-linear, or systems with a long time delay.
Nowadays, conventional controller techniques like PID solve with acceptable results most of the control problems in modern industry. However, to fulfill high-performance specifications dealing with constrained, multivariable and/or non linear systems, the process model must be included into the controller structure. MBPC has been well accepted in industry due to the generality of the method and a large number of industrial applications has already been introduced.
In this paper a fuzzy model-based predictive control algorithm is presented as a case study for a continuous-time stirred reactor with two first-order irreversible parallel exothermic reactions. Chemical reactors with exothermic reactions represent the most dangerous operational units in the chemical industry. Steady-state analysis of the reactor is presented at first and it follows from this analysis the reactor is a multiple steady-state system with one unstable and two stable steady states. The temperature control is a real problem for conventional PID controllers in this case, and fuzzy MBPC is one of the possibilities how to solve it. In this approach, a fuzzy model in the state-space domain gives a prediction of the plant output. The fuzzy predictive control approach is compared to a nonlinear model predictive control based on an optimization. Simulation results show that fuzzy predictive control gives promising results.
See the full pdf manuscript of the abstract.
Presented Tuesday 18, 13:30 to 15:00, in session Process Control (T4-8P).
