470100 On the Identification of Meta-Models for the Optimization of Grade Transition in Polymerization Processes
We approach the problem by first developing a meta-model of the detailed knowledge-driven model and quantify its performance in the grade transition optimization problem, from the prediction accuracy and the needed computational time points of view. A set of systematically selected in silico experiments, using the Design of Experiments approach [5, 6], is used to generate data for estimating the parameters of the Response Surface Model, the meta-model. The parameter estimation task aims to match the detailed model’s predictions to those of the meta-model in the desired regions of operation. Analysis of variance concepts are used to assess the accuracy of the meta-model.
The optimal operating conditions, depending on the Hydrogen feeding profile, and leading to minimum amount of the off-spec product are determined using the detailed knowledge-driven model and its meta-model approximation. We compare the meta-model predictions against those of the knowledge-driven model concerning the amount of off-spec product and the needed time for transition. This is done in six grade transition examples covering the entire range of desired polymer grades. In all six transitions, the differences between the meta-model predictions and the knowledge-driven model’s calculations are within 3%. More importantly, the computational time for the optimization using the meta-model is, on the average, 50 times smaller than the computational time required for solving the same problem using the knowledge-driven model. We also discuss how such a meta-model can be complemented to fit the plant’s behaviour more accurately utilizing historical data on the expected and achieved plant behaviours.
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