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Enhancing model predictive control using dynamic data reconciliation

Abu-el-zeet, Z.H., Roberts, P.D. and Becerra, V. M. (2002) Enhancing model predictive control using dynamic data reconciliation. AIChE Journal, 48 (2). pp. 324-333. ISSN 0001-1541

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To link to this article DOI: 10.1002/aic.690480216

Abstract/Summary

The use of data reconciliation techniques can considerably reduce the inaccuracy of process data due to measurement errors. This in turn results in improved control system performance and process knowledge. Dynamic data reconciliation techniques are applied to a model-based predictive control scheme. It is shown through simulations on a chemical reactor system that the overall performance of the model-based predictive controller is enhanced considerably when data reconciliation is applied. The dynamic data reconciliation techniques used include a combined strategy for the simultaneous identification of outliers and systematic bias.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:19191
Publisher:Wiley-Blackwell

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