Enhancing model predictive control using dynamic data reconciliation

Z.H. Abu-el-zeet, P.D. Roberts, Victor Manuel Becerra

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)324-333
    Number of pages10
    JournalAIChE Journal
    Issue number2
    Publication statusPublished - 1 Feb 2002


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