Knowledge discovery from process operational data using PCA and fuzzy clustering

An industrial case study is presented which uses principal component analysis and fuzzy c-means clustering to identity operational spaces and develop operational strategies for manufacturing desired products. Analysis of 303 data cases collected from a refinery fluid catalytic cracking process revealed that the data can be projected to four operational zones in the reduced two-dimensional plane. Three zones were found to correspond to three different product grades and the fourth is a zone corresponding to product changeover. Variable contribution analysis was also carried out to identify the most important variables that are responsible for the observed operational spaces and consequently strategies were developed for monitoring and operating the process in order to be able to move the operation from producing one product grade to another, with minimum time delays. (C) 2002 Elsevier Science Ltd. All rights reserved.