Understanding the Software Options

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ture is not expected to be any brighter.
Process testing. Testing a process
with a 5-min response time is usually
easy, but testing a process with a 5-h
response time is far more difficult.
Properly conducting the test is crucial
— this is “garbage in, garbage out”
business. A poor test gives poor quality
data; the model derived from these
data does not accurately represent
process behavior; control logic based
on this model performs poorly.
Realistically, is it possible to conduct
a test on a process with a 5-h response
time? The answer is “yes, but with
great difficulty”. Such endeavors will
be undertaken only when there is a
significant incentive to do so. Commissioning
MPC requires a process test to
determine process behavior, which is
a major component of the MPC’s total
cost. The potential benefits of the process
optimization made possible by
MPC can easily justify the cost, but
when it comes to activities such as
controller tuning, anything other than
simple tests can only be justified in
those loops critical to process operations
(often temperature loops where
derivative is likely to be used).
Dynamic modeling. The utility of
steady-state models is well-estab-
lished — all modern process designs
are based on such models. However,
the utility of dynamic models remains
debatable. The technology to develop a
dynamic model for any industrial process
has been available for at least 20
years. However, dynamic modeling is
usually applied to selected parts of the
process (with “selected parts” sometimes
being none). And even for the
selected parts, the dynamic modeling
is not always to the detail required to
tune a control loop.
The practices are entirely different
in the aerospace industry — detailed
dynamic simulations are developed for
each vehicle. Why is this not the practice
in the process industries? There is
a major difference in the ground rules.
When an aerospace vehicle leaves the
ground, every loop must be in automatic
and working, even on the first
flight. Except for a few fast loops (one
example being compressor surge control),
process plants can be operated
with every loop in manual. This is not
a popular way to do it — more people
are required and the plant does not
perform as well — but something that
is tolerable during startup. Basically,
aerospace has to start up on automatic;
process can start up on manual.
Since 1968
When will all process plants be simulated
in detail? The day they have to
start up in automatic mode.
Understand the process
Senior people in process control take
great delight in giving this advice to
new hires. And it is true. But there is a
problem. This is strategic advice that
works well at 30,000 ft and above. But
exactly what does this mean to someone
working at ground level, that is,
someone with a specific problem to
solve? New hires should not hesitate
asking “exactly how do you do that?”
The answer is often by examples (also
called “war stories”).
But understanding the process is
crucial to process control. In some respects,
artificial intelligence is an attempt
to take a lot of data and let the
system make sense of it. Based on the
test data, MPC develops finite response
models to characterize the process. But
the utility of these models depends on
the quality of the test data. How does
one assess the quality of these models?
One looks at how they respond
to certain changes and assesses their
behavior in light of past experiences
regarding the behavior of the process.
Obviously the better one understands
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