PhD ThesisMost Statistical Process Control (SPC) research has focused on the development of
charting techniques for process monitoring. Unfortunately, little attention has been paid
to the importance of bringing the process in control automatically via these charting
techniques. This thesis shows that by drawing upon concepts from Automatic Process
Control (APC), it is possible to devise schemes whereby the process is monitored and
automatically controlled via SPC procedures. It is shown that Partial Correlation
Analysis (PCorrA) or Principal Component Analysis (PCA) can be used to determine
the variables that have to be monitored and manipulated as well as the corresponding
control laws.
We call this proposed procedure Active SPC and the capabilities of various strategies
that arise are demonstrated by application to a simulated reaction process. Reactor
product concentration was controlled using different manipulated input configurations
e.g. manipulating all input variables, manipulating only two input variables, and
manipulating only a single input variable. The last two manipulating schemes consider
the cases when all input variables can be measured on-line but not all can be
manipulated on-line. Different types of control charts are also tested with the new
Active SPC method e.g. Shewhart chart with action limits; Shewhart chart with action
and warning limits for individual observations, and lastly the Exponentially Weighted
Moving Average control chart. The effects of calculating control limits on-line to
accommodate possible changes in process characteristics were also studied.
The results indicate that the use of the Exponentially Weighted Moving Average control
chart, with limits calculated using Partial Correlations, showed the best promise for
further development. It is also shown that this particular combination could provide
better performance than the common Proportional Integral (PI) controller when
manipulations incur costs.Commonwealth Scholarship Commission:
British Council:
Universiti Telcnologi, Malaysia