Abstract
Inferential estimation of polymer quality in a batch polymerization reactor using neural networks is described in this paper. Number average molecular weights, weight average molecular weights, polymerization rate, and conversion are estimated from reactor temperature, jacket inlet and outlet temperatures, and the coolant flow rate through the jacket which are easily measurable. Neural networks with mixed types of hidden neurons are used in this study and they are trained by a sequential orthogonal training algorithm. The results reported in this paper demonstrate that difficult-to-measure polymer quality variables can be estimated with acceptable accuracy from easy-to-measure process variables.
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