Adaptive Estimation of Difficult-to-Measure Process Variables
| Autor: | Željko Hocenski, Ratko Grbic, Dražen Slišković |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Physics
020401 chemical engineering General Computer Science Control and Systems Engineering Speech recognition 020208 electrical & electronic engineering Statistics 0202 electrical engineering electronic engineering information engineering Moving window Recursive algorithms Process variable estimation Adaptive estimator JITL algorithm 02 engineering and technology 0204 chemical engineering |
| Zdroj: | Automatika. 54:166-177 |
| ISSN: | 1848-3380 0005-1144 |
| DOI: | 10.7305/automatika.54-2.147 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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