Unmeasured process variables or parameters caused by cost consideration or technical unfeasibility can be mostly estimated using data reconciliation techniques. Since, however, the gross errors possibly present in the process measurements deteriorate the data reconciliation results, the reconciled estimates may be biased solutions that are different from the true values. In this paper, the enhanced data reconciliation and gross error detection method, modified MIMT using NLP, was applied to a flash distillation system. It calculated the reconciled values of the measurements as well as the optimal estimates of stage efficiencies which were not measured. These techniques using NLP showed the robustness when compared to the conventional algorithms using linearization techniques.

Data Reconciliation Gross Error Detection

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