Loop optimization:
Troubleshooting
Diagnose Loop Behavior to Find and Correct
Problems With Final Control Elements, the Environment, and
Upstream Systems Before You Tune the Controller.
By Michel Ruel, P.E.
Reprinted with permission from CONTROL Magazine, April 1999
Plant efficiency and consistent product quality depend on
proper loop performance, but PID tuning is only the last step.
This is the second in a three-part series on loop optimization.
In March, Part I discussed defining your objectives and
understanding the limitations of equipment. Next month, Part III
will cover PID tuning.
Before tuning a PID
controller, it's wise to perform a series of tests on the
loop to find any conditions that would compromise its
performance, and correct those conditions if possible to make the
tuning more effective.
Last month we mentioned the questions that should be answered
before PID tuning:
1. Process gain: Is the control valve sized properly?
2. Are hysteresis or stiction excessive?
3. Is the dead time short enough?
4. Is there an excessive amount of noise in the loop?
5. How nonlinear is the loop?
6. Asymmetry: Does the loop respond differently in one direction than in the other?
7. Is the loop optimally tuned?
These questions can be answered through a series of tests.
The Tests
Testing is performed by collecting data with the controller in
automatic mode under normal operating conditions, then
introducing a step change. For further diagnosis, data can be
collected with the loop in manual mode for comparison.
When collecting data, the scan time must be smaller than or
equal to the update time in the controller, and the update time
should be smaller than the equivalent dead time of the loop. In
many controllers, the update time
is user-selectable.
Collected data will show the operating range and performance
of the final control element. Is the controller output operating
at one end of the range? Is the valve operating near its seat?
Does the controller output change by a very small amount? If so,
the valve or final control element may need to be resized to give
better controller output resolution.
Does the loop cycle? If the loop cycles in automatic but not
in manual mode, the cause of the cycle is the closed loop. The
cycling may be due to hysteresis, nonlinearities, or poor tuning.
A cycle in a linear loop caused by poor tuning will
look sinusoidal. A sawtooth-shaped cycle can be caused by
stiction or by nonlinearity.
Cycling due to hysteresis usually has a longer period when the
process variable is near the setpoint. As the error is reduced,
the controller output change is gradually reduced and the effect
of hysteresis becomes more important.
Loop tuning software can ease the collection, presentation,
and interpretation of data. The following example of loop
analysis was done using Multi-Loop Tuner from ExperTune,
Hartland, Wis.
Sleuth Out Cycling
Tests were performed on a steam pressure control loop in a
paper mill where operators complained about poor performance,
cycling, and instability. The loop was taking more than 30
seconds to reach the new value after a setpoint change (Figure
1). This loop could not be shut down.
First, the loop was observed for two minutes in automatic
(Figure 2). The variability was 0.59% and oscillations are
present at 30 seconds and five seconds. If properly tuned, the
loop will handle the 30-second cycling, but the five-second
cycling is too fast and must be eliminated at
its origin.
Next, process variable data was collected for some time with
the controller in manual. Along with determination of the noise
band and variability in manual mode, this allowed a power
spectral density analysis (Figure 3), which can reveal hidden
cycling from an upstream process control loop or from mechanical
problems.
The power spectral density graph gives the content of the
process variable at each frequency. These hidden oscillations
could be from other loops or generated by the
tuning parameters. Cycling can also be due to periodic load
disturbances.
It is important to identify and minimize or eliminate cyclic
upsets. Do not expect the controller to remove a cyclic upset
caused upstream unless this cycling is slow in comparison with
the loop dynamics.
You may need to run power spectral densities on upstream
loops, one at a time, moving farther and farther back, until the
source of the oscillation is found. Look for a spike in the power
spectral density at the same frequency as the oscillation in the
loop. A cross-correlation analysis may help to pinpoint the
upstream loop you are looking for.
On this steam loop, the five-second cycling was from a relief
valve, which was to be checked at the next shutdown. The
30-second cycling might also be from a mechanical problem--the
loops in that part of the process were analyzed and tuned, and
none of them were implied in that cycling.
START WITH SETPOINT
Tests were performed on
a steam pressure control loop in a paper mill where operators
complained about poor performance, cycling, and instability. The
loop was taking more than 30 seconds to reach the new value after
a setpoint change. This loop could not be shut down.
CONSTANT SETPOINT
Observing the loop for
two minutes in automatic showed that variability was 0.59% and
oscillations were present at 30 seconds and five seconds. If
properly tuned, the loop will handle the 30-second cycling, but
the five-second cycling is too fast and must be eliminated at its
origin.
POWER SPECTRAL DENSITY
The power spectral
density graph gives the content of the process variable at each
frequency. It can reveal hidden cycling from an upstream process
control loop or mechanical problems. Oscillations also can be
generated by the tuning parameters or periodic load disturbances.
Smooth Out Response
When any cycling in manual mode has been minimized, take a new
set of readings in automatic. This step is optional, but can be
very useful. Does the controller increase or decrease the
performance? Is the variability greater in automatic mode? Does
cycling appear in automatic mode (controller tuned too
aggressively)?
How noisy is the measurement signal? If the noise is larger
than 2-3%, a measurement filter may improve control. Since the
derivative action of a PID controller works on the derivative of
the signal, any noise in the process is greatly amplified when
derivative action is used. A filter may allow you to add
derivative to loops, which can significantly improve performance.
Check the process gain (Figure 4). In this case, the process
responds well and noise is small, but the process gain is very
high. While the controller output change is 3%, the process
variable change is 23%. The process gain is around eight, and
this is definitely too high.
In manual mode, check the hysteresis and stiction of the loop.
For the hysteresis check, make several controller output changes:
two steps in one direction and one step in the other (Figure 5).
Finally, to detect stiction, make a very small fourth step (or a
series of steps).
Using the data, run a hysteresis check on the loop. If the
hysteresis is more than 1% for valves with positioners and 3% for
valves without positioners, you should repair or change
equipment. Hysteresis of 1-4% degrades loop performance, while
with tight tuning, hysteresis greater than 3% causes
oscillations.
The stiction test, a series of small steps (0.5%) in the
controller output, shows the amount of change needed before the
valve really moves (as indicated by a change in the process
variable).
NOISE AND GAIN
Here, the process
responds well and noise is small, but the process gain is very
high. A controller output change of 3% changes the process
variable 23%. The process gain is about eight, and this is
definitely too high.
HYSTERESIS CHECK
To check hysteresis,
make two controller output changes in one direction and one step
in the other. This valve has little hysteresis, but the process
gain is high, so it is easy to observe the hysteresis effect.
Is It Linear?
To determine linearity, run the loop in manual or automatic
and let it settle at several different locations in the
controller output range (Figure 6). If in manual mode, 15% steps
starting at 5% work well, for example, at 5%, 20%, 35%, 50%, 65%,
80%, and 95%.
You can run these tests in automatic if both the measurement
and output reach a full settled condition after each step. If in
automatic mode, the setpoint should be varied from the minimum to
the maximum allowable.
Of course, this step is not always possible. If it must be
skipped for process considerations, be careful when tuning the
controller. A safety factor is usually applied when selecting the
tuning parameters if the behavior of the process is unknown
outside the range of previous steps.
LINEARIZATION CHECK
To determine linearity,
let the loop settle at several different locations in the
controller output range. If the ratio of the highest to lowest
gain is more than three, add (or modify any existing) output
characterization.
Graph the process characteristic from the data collected at
various settled areas. How linear is the process? Look for the
lowest and highest-slope areas. The lowest slope is the lowest
gain; the highest is largest gain. The ratio of the highest gain
to lowest gain should be no more than three and preferably less
than two.
If the ratio is higher than three, you should add (or modify
any existing) output characterization to the loop, which computes
X-Y pair values or uses an equation to compensate for gain.
An output characterizer can greatly benefit a split-range
control loop. Split-range loops switch between two or more valves
depending on the controller output--for example, below 50% output
the loop is cooling with chilled water or heat-exchanged oil;
above 50% it's heated with steam, hot water, or furnace-
heated oil. These loops are usually highly nonlinear.
Do not use an output characterizer to linearize pH
loops--these require input characterization. With such loops, use
gain scheduling based on the process variable or the error.
Check Symmetry
Next, check for asymmetry in manual or automatic mode. Perform
step tests in the opposite direction from the last step or,
preferably, repeat the steps in the opposite direction. Does the
process respond differently in the up direction versus the down?
If so, can you reduce or eliminate the discrepancy? Asymmetry
occurs, for example, with a spring and diaphragm valve where the
pressure is applied to move the valve in one direction and the
spring is used in the other direction.
If you cannot eliminate the asymmetry, you must use the more
conservative tuning or special algorithms that tune the
controller differently depending on the direction.
Based on the above tests, you may need to do maintenance on
the valve, add filtering, linearize the loop, repair or maintain
a sensor, or identify and remove upstream cyclic upsets.
The last step is to identify the highest-gain,
largest-deadtime location in the loop and tune the loop based on
that worst case. Loop tuning will be covered next month in the
third and final installment in this series.
Michel Ruel, P.E., at TOP Control Inc., St. Romuald, Quebec,
has 22 years of plant experience at companies including Monsanto,
Domtar Paper, Dow Corning, and Shell Oil. Author of several
publications on instrumentation and control and frequent
university lecturer, Ruel is experienced in solving unusual
process control problems and a pioneer in implementing fuzzy
logic in process control. His e-mail address is mruel@topcontrol.com .
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