Adaptive Estimation of Difficult-to-Measure Process Variables (CROSBI ID 192285)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Slišković, Dražen ; Grbić, Ratko ; Hocenski, Željko
engleski
Adaptive Estimation of Difficult-to-Measure Process Variables
There exist many problems regarding process control in the process industry since some of the important variables cannot be measured online. This problem can be significantly solved by estimating these difficult-to-measure process variables. In doing so, the estimator is in fact an appropriate mathematical model of the process which, based on information about easy-to-measure process variables, estimates the current value of the difficult-to-measure variable. Since processes are usually time-varying, the precision of the estimation based on the process model which is built on old data is decreasing over time. To avoid estimator accuracy degradation, model parameters should be continuously updated in order to track process behavior. There are a couple of methods available for updating model parameters depending on the type of process model. In this paper, PLSR process model is chosen as the basis of the difficult-to-measure process variable estimator while its parameters are updated in several ways – by the moving window method, recursive NIPALS algorithm, recursive kernel algorithm and Just-in-Time learning algorithm. Properties of these adaptive methods are explored on a simulated example. Additionally, the methods are analyzed in terms of computational load and memory requirements.
Process variable estimation; Adaptive estimator; Moving window; Recursive algorithms; JITL algorithm
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Podaci o izdanju
54 (2)
2013.
166-177
objavljeno
0005-1144
10.7305/automatika.54-2.147
Povezanost rada
Računarstvo, Temeljne tehničke znanosti