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.

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This paper presents methods for the statistical analysis of plant operations optimization results with special consideration for real-time optimization (RTO) applications. The key challenge is to determine whether the results of an optimization calculation should be implemented in the plant. Since feedback data used to correct the model include noise and the effects of high-frequency disturbances, the results of the model-based optimization calculations are corrupted by a stochastic component. The methods developed in this paper apply multivariable statistical hypothesis tests based on control charts in order to distinguish between high-frequency disturbances propagated through the calculations and significant changes in the plant optimization variables with the goals of reducing the frequency of unnecessary changes in the implemented independent optimization variables and increasing plant profits. Only the statistically significant results are implemented in the plant. Case studies indicate that increased profit can be obtained by implementing fewer changes to the process because the preponderance of changes due to noise are rejected whereas most meaningful changes are implemented.