March 2008

Special Report: Instruments and Networks

Novel approach for process plant monitoring

Using statistical data compression important process changes can be quickly detected and identified

Al-Baiyaa, M., Lenka, C., Jubail United Petrochemical Co., Sabic; Khalfe, N., Lahiri, S. K., National Institute of Technology

Often it is time-consuming to monitor conditions in modern complex process plants since there is an abundance of instrumentation that measures thousands of process variables every few seconds. This has caused a "data overload" and due to the lack of appropriate analyses this wealth of information is underutilized. Operating personnel typically use only a few variables to monitor the plant's performance. Fortunately, groups of variables often move together because more than one variable may be measuring the same driving principle governing the process behavior. Multivariate statistical methods such as principal component analysis (PCA) are capable of compressing the information into low-dim

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