1384200

11093

Statistical Process Control (SPC) provides a tool for achieving and maintaining product quality. In todayʹs climate of major data monitoring campaigns there has been an increase in interest in the multivariate statistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. Within univariate SPC, techniques for identifying when a process is moving out of control are well established. Similar guidelines are required for multivariate statistical process control (MSPC). Two approaches will be discussed - Hotellingʹs T2 statistic and a new approach, the M2 statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor.

349

Fault detection and diagnosis , Confidence bounds , multivariate statistical process control

Studia Iranica

358

358