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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

figure1

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

f2

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

figure3

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

figure4

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

figure4

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

figure4

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.


 

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