Abstract
A method for predicting unmeasured process variables in poorly-known chemical processes is presented. The effectiveness of the method is demonstrated on a simulation example of a realistic batch reactor, where only 70% of the reactant consumption and 30% of the reaction-heat generation are accounted for by the known model. The goal is to obtain an on-line concentration predictor using only a limited number of imperfect concentration measurements, laboriously made available for only a few costly ‘modeling’ runs. The proposed combination of a feedback neural network and the available a-priori information is shown to satisfactorily solve this challenging prediction problem.
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