Nonlinear Model Predictive Control based on Existing Mechanistic Models of Polymerisation Reactors

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Model Predictive Control (MPC) is established as the most powerful and most successful method for multivariable control in the process industries. Most industrial applications of MPC rely on linear dynamic process models that are identified from active experiments on the plant. If a rigorous mechanistic model of the respective process unit already exists, it would be attractive to use this model directly inside an MPC algorithm. The PSE software “gPROMS Nonlinear Model Predictive Controller” (gNLMPC) provides precisely this functionality, and this paper describes its application to a polymerization reactor. Properties, features and advantages of linear and nonlinear MPC are compared systematically.