Abstract
A systematic procedure for the design and implementation of a multivariable controller for a commercial scale polymerization reactor is presented. The reactor characteristics are typical of many industrial processes, including unmeasured disturbances, significant deadtimes, and sampling and measurement errors. Closed loop experimentation is used to generate appropriate input/output data. Principal component analysis coupled with partial least squares regression techniques are applied to the data to reduce the dimensionality of the identification and control problem. An identified discrete transfer function model was formulated and successfully implemented in an internal model controller structure. Results indicate a reduction in polymer product variability attributed to consistent feedback manipulations and identification of output variable dependency.
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