Data Preprocessing in Data Based Process Modeling

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.