Multivariate statistical monitoring of process operating performance

Canadian Journal of Chemical Engineering - Tập 69 Số 1 - Trang 35-47 - 1991
J.V. Kresta1, John F. MacGregor1, Thomas E. Marlin1
1Chemical Engineering Department, McMaster University, Hamilton, Ontario L8S 4L7

Tóm tắt

AbstractProcess 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.

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Canadian Journal of Chemical Engineering

Tập 69 Số 1

35-47

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