This report takes a non-technical
look at the state-of-the-art in modern control engineering, focusing
on techniques that are applicable to the process industries. As
the rate of development in this field is phenomenal, the review
is not exhaustive. What we have done is to draw upon the experiences
of the Advanced
Process Control Group at the School of Chemical Engineering and Advanced Materials, Newcastle University. which has been extensively involved
in the fundamental development and application of modern control
methods for nearly two decades.
It is also well known that
any improvement in the performance of control strategies will
result in more consistent production, facilitating process optimisation,
hence less re-processing of products and less waste.
Process models underpin most modern
control approaches. Depending on the model forms, different controllers
can be synthesised. Even the prevalent Proportional+Integral+Derivative
(PID) algorithm can be designed from a model based perspective.
The performance capabilities of PID algorithms are limited though.
More sophisticated strategies, such as adaptive algorithms and predictive
controllers have been proposed for improved process control. Due
to the emphasis on Quality, Statistical Process Control (SPC) techniques
are also experiencing a revival. In particular, attempts are being
made to integrate traditional SPC practice with engineering feedback
control techniques. Each of these strategies possesses respective
merits. Of special significance is the recent attention paid to
developing practicable nonlinear controllers, in recognition of
the fact that many real processes are nonlinear and that adaptive
systems may not be able to cope with significant nonlinearities.
There are two approaches. One attempts to design control strategies
based on nonlinear black box models, e.g. nonlinear time-series
or neural networks. The other relies on an analytical approach,
making use of a physical-chemical model of the process. However,
there are indications that the two approaches can be rationalised.
Cheap powerful computers and advances in the field of Artificial
Intelligence are also making their impact. Local controls are increasingly
being supplemented with monitoring, supervision and optimisation
schemes; roles that traditionally were undertaken by plant personnel.
These reside at a higher level in the information management and
process control hierarchy. Performing tasks that relate directly
to overall plant management objectives, they effectively link plant
business objectives with local unit operations. The result is an
environment that is conducive to more consistent production.
Modern process plants, designed for
flexible production and to maximise recovery of energy and material,
are becoming more complex. Process units are tightly coupled and
the failure of one unit can seriously degrade overall productivity.
This situation presents significant control problems. The literature
on relevant control, monitoring, supervision and optimisation techniques
is voluminous, each article exhorting a certain solution to a particular
problem. However, it is generally acknowledged that there is currently
not one technique that will solve all the control problems that
can manifest in modern plants. Indeed, different plants have different
A systematic studied approach
to choosing pertinent techniques and their integration into a
co-operative management and control system will significantly
enhance plant operation and profitability. This is the goal
of advanced process control.