Naslov
Application of PLS and LS-SVM in Difficult-to- Measure Process Variable Estimation

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

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

Izvornik
Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics (SISY 2010) / Aniko Szakal - Subotica : Obuda University, 2010, 313-318

ISBN
978-1-4244-7395-3

Skup
8th IEEE International Symposium on Intelligent Systems and Informatics (SISY 2010)

Mjesto i datum
Subotica, Srbija, 10.09.2010. - 11.09.2010

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
data based modeling; process variable estimation; PLS; LS-SVM

Sažetak
Very often important process variables which are concerned with the final product quality cannot be measured by a sensor or the measurements are too expensive and often not reliable. In order to enable continuous monitoring of process variables and efficient process control, soft-sensors are usually used to estimate these difficult-to- measure process variables. Soft-sensor is based upon mathematical model of the process. Process model building is based on plant data, taken from the process database. In this paper two methods, namely, Partial Least Squares (PLS) and Least Squares Support Vector Machines (LS-SVM), are used for difficult-to-measure process variables estimation. The methods are used for modeling simulated fluid storage process and oil distillation process. Results are compared and discussed. Advantages and disadvantages of each approach are outlined with respect to this specific application area. Additionally, we propose how these methods can be combined to exploit good properties of both methods.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

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