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Pregled bibliografske jedinice broj: 392803

Data Preprocessing in Data Based Process Modeling


Slišković, Dražen; Grbić, Ratko; Nyarko, Emmanuel Karlo
Data Preprocessing in Data Based Process Modeling // Proceedings of the 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing / Kayakan, Erdal (ur.).
Turkey: IFAC, International Federation for Automatic Control, 2009. (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Data Preprocessing in Data Based Process Modeling

Autori
Slišković, Dražen ; Grbić, Ratko ; Nyarko, Emmanuel Karlo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing / Kayakan, Erdal - Turkey : IFAC, International Federation for Automatic Control, 2009

ISBN
978-3-902661-66-1

Skup
The 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing

Mjesto i datum
Istanbul, Turska, 21-23.09.2009.

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Plant data preprocessing; wavelet analysis; process modeling; projection into a latent space; difficult-to-measure process variable estimation; distillation column

Sažetak
Important 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 real 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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
165-0361621-2000 - Distribuirano računalno upravljanje u transportu i industrijskim pogonima (Željko Hocenski, )

Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek