Dr Jie Zhang received his PhD in Control Engineering from City University, London,
in 1991. He has been with the School of Chemical Engineering and Advanced Materials,
Newcastle University, UK, since 1991 and is currently a Senior Lecturer and Degree
Programme Director for MSc Applied Process Control. His research interests are in the
general areas of process system engineering including process modelling, batch process
control, process monitoring, and computational intelligence. He has published over 280
papers in international journals, books, and conference proceedings (H-index of 27 based
on Web of Science). He is on the Editorial Boards of a number of journals including
Neurocomputing published by Elsevier and Control Engineering of China.
In today’s chemical and process industries, plants are becoming larger, more
complex and heavily instrumented. The requirements to manufacture products
with minimalvariations around desired quality targets and to operate safely according
to health, safety and environmental protection regulations, have become essential
due to market and public demand. The key to successful operation is efficient on
-line process monitoring, which enables the early warning of process disturbances,
process malfunctions or faults. This talk presents some multivariate statistical process
performance monitoring techniques that capitalise on the huge amount of historical
process operational data. Many industrial processes are characterised as “data rich
and information poor”. Discovering useful information through analysing the huge
historical process operational data is the key in successful process monitoring. The
bases of multivariate statistical process performance monitoring techniques are
multivariate projection techniques, such as principal component analysis (PCA), partial
least squares (PLS), multidimensional scaling (MDS), and canonical variate analysis
(CVA). The philosophy behind these approaches is to reduce the dimensionality of
the problem by forming a new set of latent variables to obtain an enhanced
understanding of the process behaviour. The basic techniques of multivariate
statistical process monitoring will be presented. Techniques for coping with nonlinear
processes, batch processes, processes with multiple operating modes, and fault
diagnosis will also be discussed. Fault diagnosis using machine learning tools such as
neural networks and neuro-fuzzy networks will be discussed.
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