Abstract
A model‐based predictive control system is designed for a copolymerization reactor. These processes typically have such a high nonlinear dynamic behavior to make practically ineffective the conventional control techniques, still so widespread in process and polymer industries. A predictive controller is adopted in this work, given the success this family of controllers is having in many chemical processes and oil refineries, especially due to their possibility of including bounds on both manipulated and controlled variables. The solution copolymerization of methyl methacrylate with vinyl acetate in a continuous stirred tank reactor is considered as an industrial case study for the analysis of the predictive control robustness in the field of petrochemical and polymer production. Both regulatory and servo problems scenarios are considered to check tangible benefits deriving from model‐based predictive controller implementation.
Highlights
Operations management of polymerization plants is quite complex, since such processes are characterized by strong nonlinearities, intense state variable interactions, and wide variations in operating conditions
A predictive controller is adopted in this work, given the success this family of controllers is having in many chemical processes and oil refineries, especially due to their possibility of including bounds on both manipulated and controlled variables
The solution copolymerization of methyl methacrylate with vinyl acetate in a continuous stirred tank reactor is considered as an industrial case study for the analysis of the predictive control robustness in the field of petrochemical and polymer production
Summary
Operations management of polymerization plants is quite complex, since such processes are characterized by strong nonlinearities, intense state variable interactions, and wide variations in operating conditions. The name MPC arises from use an explicit prediction model of the process to be controlled so as to foresee its future behavior Such a capacity of predicting process behavior can be exploited to get the so-called online optimal control, where tracking error, that is, the difference between the predicted output and the desired reference trajectory, is minimized over a future horizon. A DMC procedure uses the system information in the context of an optimizer that solves the control problem for the trajectory of the manipulated variable over a future time horizon based on a dynamic model of the process. Peterson et al [22] developed a control algorithm that uses an explicit nonlinear process model and the basic elements of the classical DMC and applied it to a semibatch polymerization reactor. A nonlinear dynamic model of the system is used to simulate both regulatory and servo responses of DMC
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