Article ID Journal Published Year Pages File Type
710284 IFAC Proceedings Volumes 2009 6 Pages PDF
Abstract

AbstractImportant process variables which give information about the final product quality cannot often be measured by a sensor. The alternative procedure is estimation of these difficult-to-measure process variables for which it is necessary to have an appropriate process model. Process model building is based on plant data, taken from the process database. Since the quality of the built model depends heavily on the modeling data informativity, a preparatory part of modeling, in which analysis and preprocessing of available measured data are performed, is a very important step in such process modeling. The analysis and preprocessing of plant data obtained from an oil distillation process are showed in the paper. The results show that, apart from the regression method applied, selection of easy-to-measure variables which will be used in the model building and filtering of easy-to-measure variables significantly affects process model prediction capabilities.

Related Topics
Physical Sciences and Engineering Engineering Computational Mechanics
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