Technical gazette, Vol. 18 No. 1, 2011.
Original scientific paper
Online data preprocessing in the adaptive process model building based on plant data
Dražen Slišković
; Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, KnezaTrpimira 2B, HR-31000 Osijek, Croatia
Ratko Grbić
; Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, KnezaTrpimira 2B, HR-31000 Osijek, Croatia
Željko Hocenski
; Faculty of Electrical Engineering, J. J. Strossmayer University of Osijek, KnezaTrpimira 2B, HR-31000 Osijek, Croatia
Abstract
Process variables which are concerned with the quality of final product cannot often be measured by a sensor. The alternative procedure is the estimation of these difficult-to-measure process variables for which it is necessary to have an appropriate process model. Process model building, based on plant data taken from the process database, is usually the most cost-effective way to obtain a process model. Since the quality of the built model depends heavily on the modelling data informativity, preprocessing of the available measured data is an important step in such process modelling. Processes are usually time-varying and non-stationary, so that the precision of the estimation based on process model with constant parameters degrades over time. Because of that, model parameters have to be updated online. However, in order to successfully keep the precision of the estimation, it is important to use the samples which do not contain errors in the parameter updating procedure which requires a quality online data preprocessing. The online data preprocessing and online model parameter updating are discussed and presented on two examples and the influence of data preprocessing on adaptive process model quality is analyzed.
Keywords
difficult-to-measure variable estimation; online data preprocessing; online model parameter updating; plant data
Hrčak ID:
65923
URI
Publication date:
31.3.2011.
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