Estimation of alkyd reactors with discrete-delayed measurements

This work addresses the problem of designing an on-line dynamic data processor to estimate and predict viscosity, conversion, and molecular weight of an alkyd reactor through discrete-delayed measurements of viscosity. This estimation–prediction scheme is important to monitor, control and stop the batch operation so that its yield is maximized with a product grade within specifications. Since the alkyd polymerization involves a complex reaction of synthetic or natural fatty acids with unknown detailed kinetics, the proposed estimator is designed on the basis of a simple kinetics model with an on-line adjusted observable parameter. The resulting estimator has a systematic construction-tuning procedure coupled with a robust convergence criterion, and its functioning and performance is illustrated with experimental data.