Abstract
The proper implementation of advanced control schemes for complex chemical processes heavily rely on the availability of appropriate mathematical models. The main objective of this paper is to show through examples how process models can be inserted into advanced controllers to allow the successful control of the process when the controlled variables are not measured or are measured infrequently. The control strategies are illustrated with actual data obtained for two typical polymerization processes. In the first example, a control algorithm is designed and implemented experimentally for the simultaneous closed-loop control of composition and average molecular weight of a copolymer latex. In the second case, a control scheme is designed for the simultaneous control of polymer production, polymer composition and melting index (MI) in a solution ethylene Ziegler-Natta polymerization process.
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