Abstract
Reliable sets of steady-state component and total flow rate data form the cornerstone for the monitoring of plant performance. The detection and isolation of gross errors in these data constitute an essential part of the process of reconciliation of the measurement data, which are generally inconsistent with process constraints. By using a neural net to classify measurement or constraint residuals, gross errors in the data can be identified accurately and efficiently. Gross error detection and isolation with artificial neural nets do not require explicit knowledge of the distribution of random errors in measurement values and can be applied to processes with arbitrary constraints.
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