Multivariate data analysis applied to low-density polyethylene reactors

Skagerberg, B., MacGregor, J.F. and Kiparisides, C., 1992. Multivariate data analysis applied to low density polyethylene reactors, Chemometrics and Intelligent Laboratory Systems, 14: 341–356.In this paper we discuss how partial least squares regression (PLS) can be applied to the analysis of complex process data. PLS models are here used to: (i) accomplish a better understanding of the underlying relations of the process; (ii) monitor the performance of the process by means of multivariate control charts; and (iii) build predictive models for inferential control. The strategies for applying PLS to process data are described in detail and illustrated by an example in which low-density polyethylene production is simulated.