PhD ThesisThe thesis describes the application of Multivariate Statistical Process Control
(MSPC) to chemical processes for the task of process performance monitoring and
fault detection and diagnosis. The applications considered are based upon
polymerisation systems. The first part of the work establishes the appropriateness of
MSPC methodologies for application to modern industrial chemical processes. The
statistical projection techniques of Principal Component Analysis and Projection to
Latent Structures are considered to be suitable for analysing the multivariate data sets
obtained from chemical processes and are coupled with methods and techniques for
implementing MSPC. A comprehensive derivation of these techniques are presented.
The second part introduces the procedures that require to be followed for the
appropriate implementation of MSPC-based schemes for process monitoring, fault
detection and diagnosis. Extensions of the available projection techniques that can
handle specific types of chemical processes, such as those that exhibit non-linear
characteristics or comprise many distinct units are also presented. Moreover, the
novel technique of Inverse Projection to Latent Structures that extends the
application of MSPC-based schemes to processes where minimal process data is
available is introduced. Finally, the proposed techniques and methodologies are
illustrated by applications to a batch and a continuous polymerisation process.BR1TE EURAM CT 93 0523 (INTELPOL:
ESPRTT PROJECT 22281 (PROGNOSIS):
Centre of Process Analysis, Chemometrics and Control, University of Newcastle:
Chemical Process Engineering Research Institute, Thessaloniki, Greece
