Multivariate statistical monitoring of process operating performance

Abstract

Process computers routinely collect hundreds to thousands of pieces of data from a multitude of plant sensors every few seconds. This has caused a “data overload” and due to the lack of appropriate analyses very little is currently being done to utilize this wealth of information. Operating personnel typically use only a few variables to monitor the plant's performance. However, multivariate statistical methods such as PLS (Partial Least Squares or Projection to Latent Structures) and PCA (Principal Component Analysis) are capable of compressing the information down into low dimensional spaces which retain most of the information. Using this method of statistical data compression a multivariate monitoring procedure analogous to the univariate Shewart Chart has been developed to efficiently monitor the performance of large processes, and to rapidly detect and identify important process changes. This procedure is demonstrated using simulations of two processes, a fluidized bed reactor and an extractive distillation column.

Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

“…In an earlier work, Kresta et al (1991) laid out the basic methodology of using multivariate statistical process control procedure to handle large numbers of process and quality variables for continuous process. Later on, Nomikos and MacGregor (1994) extended the use of multivariate projection methods to batch processes by using multiway PCA.…”

Multivariate Modeling of a Chemical Toner Manufacturing Process

“…In these methods the data are compressed to extract the information from them. Some of the schemes for multivariate procedures for monitoring continuous processes are of (Kresta 1991) and (MacGregor, Jaeckle, et al 1994). In these methods the variation in the trajectories of historical reference distribution of normal batches are characterized by projection of the data into a lower dimensional space, (data compression), principal component space.…”

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

“…For Hotelling's T 2 , the variations of the mean and the covariance matrix are generally two essential factors that represent the process changes from a normal situation to a situation with several faults (Kresta et al, 1991). Each PC's contribution to the T 2 index comes from two parts, namely, the mean or variance change of the PC itself and the relationship with other PCs responsible for the process change.…”