Closed-loop stochastic model predictive control in a receding horizon implementation on a continuous polymerization reactor example

We evaluate closed-loop stochastic model predictive control techniques on a nonlinear high density polyethylene fluidized bed example. Closed-loop MPC is a control strategy in which one optimizes feedforward signals while maintaining back-off to inequality constraints on the process variables. This...

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Bibliographic Details
Main Authors: van Hessem, D.H., Bosgra, O.H.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Centralized control
Computer science
control theory
systems
Constraint optimization
Control theory. Systems
Exact sciences and technology
Fluidization
Inductors
Polyethylene
Polymers
Predictive control
Predictive models
Signal processing
Stochastic processes
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Summary:We evaluate closed-loop stochastic model predictive control techniques on a nonlinear high density polyethylene fluidized bed example. Closed-loop MPC is a control strategy in which one optimizes feedforward signals while maintaining back-off to inequality constraints on the process variables. This back-off is kept minimal by using so-called closed-loop model prediction in which control plays a central role. The objective in this paper is to illustrate these novel techniques on a realistic simulator of an industrial size HDPE plant by enabling a grade change under persistent disturbances in the feeds.
ISSN:0743-1619
2378-5861
DOI:10.23919/ACC.2004.1383723