Abstract
In this paper we apply a multivariable nonlinear quadratic dynamic matrix control (NL-QDMC) strategy to the control of a styrene polymerization reactor. NL-QDMC is an extension of a well-known technique for handling constrained processes based on linear models (QDMC) to nonlinear models. The NL-QDMC algorithm incorporates an Extended Kalman Filter (EKF) to handle state variable and parameter estimation; also included is an integrated white noise disturbance model. We consider (through simulations) the control of polymer properties such as number average molecular weight (NAMW) and polydispersity or branching. Temperature and volume measurements are available frequently while concentration and molecular weight distribution measurements are only available every 30 minutes with a delay of 30 minutes. There will always be both parametric and structural uncertainty in any model-based estimation and control scheme. We illustrate the effect of model structure uncertainty by using a simplified model for the control strategy, and a more complex model for the “plant” (incorporating the “gel effect”, for example).
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Schley, M., Prasad, V., Russo, L.P., Bequette, B.W. (2000). Nonlinear Model Predictive Control of A Styrene Polymerization Reactor. In: Allgöwer, F., Zheng, A. (eds) Nonlinear Model Predictive Control. Progress in Systems and Control Theory, vol 26. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8407-5_23
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DOI: https://doi.org/10.1007/978-3-0348-8407-5_23
Publisher Name: Birkhäuser, Basel
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