Chemometric methods for process monitoring and high‐performance controller design

TLDR: A huge amount of data is collected by computer monitoring systems in the chemical process industry, and such tools as principal component analysis and partial least squares have been shown to be very effective in compressing this large volume of noisy correlated data into a subspace of much lower dimension than the original data set.

Abstract: A huge amount of data is collected by computer monitoring systems in the chemical process industry. Such tools as principal component analysis and partial least squares have been shown to be very effective in compressing this large volume of noisy correlated data into a subspace of much lower dimension than the original data set. Because most of what is eliminated is the collinearity of the original variables and the noise, the bulk of the information contained in the original data set is retained. The resulting low dimensional representation of the data set has been shown to be of great utility for process analysis and monitoring, as well as in selecting variables for control. These types of models can also be used directly in control system design. One way of approaching this is to use the loading matrices as compensators on the plant. Some advantages of using this approach as part of the overall control system design include automatic decoupling and efficient loop pairing, as well as natural handling of nonsquare systems and poorly conditioned systems.