Accurate Identification of Biased Measurements Under Serial Correlation

Chemical process data are often correlated over time (i.e., auto or serially correlated) due to recycle loops, large material inventories, sampling lag, dead time, and process dynamics created by high-order systems and transportation lag. However, many approaches that attempt to identify gross errors in measured process variables have not addressed the issue of serial correlation which can lead to large inaccuracies in identifying biased measured variables. Hence, this work extends the unbiased estimation technique (UBET) of Rollins and Davis 1 to address serial correlation. The serially correlated gross error detection study of Kao et al . 2 is used as a basis for setting up this study and comparison. In their work, the type of autocorrelation was assumed known ( ARMA (1,1)), and the measurement test (MT) was used for the identification of the measurement bias. While Kao et al . 2 used prewhitening of the data and variances of measured variables derived from knowledge of the time correlation structure, this work presents two prewhitening methods and a different identification strategy based on the UBET. Results of the simulation study show the UBET has higher perfect identification rates and lower type I error rates over the MT.