Design of Optimal Sequential Experiments to Improve Model Predictions from a Polyethylene Molecular Weight Distribution Model

Reliable model predictions require an appropriate model structure and also good parameter estimates. For good parameter estimates to be obtained, it is important that the data used in parameter estimation are informative. Alphabet-optimal experimental designs can be used to ensure that new experiments are as informative as possible. This work presents the development of D- and A-optimal sequential experimental designs for improving parameter precision in a molecular-weight-distribution model for ZieglerNatta-catalyzed polyethylene. Novel V-optimal designs techniques are developed to improve the precision of model predictions, and anticipated benefits are quantified. Problems with local minima are discussed and comparisons between the optimality criteria are made.

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