Characterization of Closed-loop Process Variable Data

Abstract

Business analysis methods for controller performance assessment are implemented using statistical process control (SPC) and "six-sigma" principles. This work focuses on the characterization closed-loop archived data primarily for use in SPC-based analysis for controller performance assessment. Closed-loop data sets for the advanced process control manipulated variables (APC-MVs) exhibit different levels of variability when considered over a one year period. These periods of variability are termed as "error variability bands." This thesis presents four error variability band identification techniques using general purpose statistical tools including histograms, normal probability plots, quantile-quantile plots and the sample autocorrelation function. The performance of these methods is presented using archived refinery data reconstructed on a one-minute sample period for flow, pressure, and temperature loops. The impact of set-point variability on APC manipulated variables is also illustrated.