Combined bias and outlier identification in dynamic data reconciliation

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

    Research output: Contribution to journalArticlepeer-review


    Measured process data normally contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, techniques for the detection and identification of both systematic bias and outliers in dynamic process data are presented. A novel technique for the detection and identification of systematic bias is formulated and presented. The problem of detection, identification and elimination of outliers is also treated using a modified version of a previously available clustering technique. These techniques are also combined to provide a global dynamic data reconciliation (DDR) strategy. The algorithms presented are tested in isolation and in combination using dynamic simulations of two continuous stirred tank reactors (CSTR).
    Original languageEnglish
    Pages (from-to)921-935
    JournalComputers & Chemical Engineering
    Issue number6
    Publication statusPublished - 15 Jun 2002


    • dynamic data reconciliation, bias detection, gross error detection, process control, supervisory control, data processing


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