By Jacques F. Smuts, Ph.D., P.E., OptiControls
Power plants—especially coal-fired boilers—pose a set of process control problems unparalleled in other industries. Rapid load ramps, continuous unit demand changes, variable fuel quality and process interactions are only a few of the factors challenging the performance of boiler control loops. Attempts to address poor performance are often limited to the application of trial-and-error tuning methods and frequently fall short of the objectives.
Although some control problems can potentially be resolved through controller tuning, poor control loop performance can have several other causes requiring different solutions. Once the origin of the problem has been correctly identified, the solution is frequently obvious. The origin of the problem can be pinpointed through a simple but systematic analysis of the control problem.
Poor control loop performance often makes itself evident as excessively large deviations between the process variable and its set point. An operator may notice the deviations on his process trend displays or process alarm system and place the controller into manual mode in an attempt to stabilize the control loop. As a result, poor control loop performance may be indicated by excessive deviations between process variable and set point or controllers being in manual mode.
Controllers in Manual
Some plants have 30 percent or more of their control loops in manual control mode. Being in manual mode does not necessarily mean the control loop performs poorly in automatic control mode. Many control loops are associated with redundant equipment, or certain operating modes, and can justifiably be in manual mode as a result. However, if a control loop is supposed to be in automatic control mode, but it is in manual mode, an investigation is required. It is important to talk to the operator to find out why the loop is in manual. Historical process trends of times when the loop was in automatic control can be reviewed to gain more insight into the problem, or the loop can be placed into automatic control mode to observe how it responds.
Deviations from Set Point
If a controller is in automatic control mode and its process variable always remains acceptably close to its set point, there should be no need for concern. However, excessively large deviations between process variable and set point indicate poor control. The problem may be constant or intermittent and could originate from within or outside the control loop, but if large deviations occur, it provides grounds for further investigation. Large deviations between process variable and set point can be caused by a rapidly changing set point, process disturbances, loop nonlinearities, interactions, control element saturation or poor controller tuning. The exact cause can be pinpointed through systematic analysis, using a fault analysis tree, like the one in Figure 2.
The first step in analyzing the control problem would be to look at the shape of the deviations on a trend plot: whether they are cyclical (oscillating) or random. This will determine the path for further analysis. Although sophisticated frequency analysis tools can be used to determine if deviations are cyclical or random, it can also be done quite simply by looking at historical time trends of the process variable. Once it has been determined if the deviations from set point are cyclical or not, the next level of analysis can be conducted.
Cyclical deviations can appear as sine, saw tooth, or square wave patterns. A cyclical deviation between set point and process variable can originate from within the control loop or it could be caused by external factors. It could also be a result of a cyclical interaction between two or more control loops. To narrow down the cause of the oscillation, the controller should be placed into manual mode to see if the oscillation would stop. If the oscillation persists when the controller is in manual mode, it originates from outside the loop.
An oscillation with its origin outside the control loop can influence the control loop through its set point or through the process.
Oscillations entering the loop through the set point are easy to find; simply look at where the loop’s set point is driven from. For example, if a feedwater flow control loop oscillates because its set point is oscillating, the problem lies with the drum level controller. The fault analysis should then be applied to the drum level controller.
One oscillating loop can cause several other loops on the same plant to oscillate with it. For example, if the steam pressure controller on a boiler oscillates, several other loops including the steam temperature will likely oscillate too. The steam temperature will keep on oscillating, even if the steam temperature controller is placed into manual control mode. The loops will all oscillate in harmony with the same period of oscillation.
Historical trends or process analysis software can be used to identify all the loops oscillating with the same period. The problem loop can then be isolated through knowledge of the boiler systems and their interactions, or by placing likely culprit loops in manual one at a time. If the loop driving the oscillations is placed into manual control mode, the oscillations will cease on all loops. This loop should then be analyzed further.
Note that this scenario is different from a cyclical interaction in which two or more control loops interact directly with each other in a cyclical fashion. In the case of a cyclical interaction, any of the participating control loops placed into manual will cause all loops to stop oscillating. This will be discussed later.
Oscillations generated by a control loop itself can be caused by faulty final control element (for example, control valve or damper) or by tuning. Generally, if the oscillation is caused by poor tuning, the process variable will oscillate with a reasonably smooth sine-wave pattern. If the oscillation is caused by final control element problems, the trends are more likely to be shaped like a square wave or saw tooth wave. However, this is a guideline and not a definitive test. If the control loop drives a final control element, the performance of the latter should be checked first before attempting to tune the controller. This is especially true if the control loop used to work properly and is now oscillating without any changes to the controller settings.
The most common equipment-based causes of oscillations are control valve (or damper) related. Note that the discussions below sometimes mention only control valves for the sake of brevity. However, dampers can cause the same problems with the same symptoms as control valves. So where only control valves are mentioned, the same arguments will also apply to dampers.
Control Valve Problems
A common problem found in final control elements is stiction. This is short for Static Friction, and means that the valve internals are sticky. If the stem of a valve with stiction comes to rest, it tends to stick in that position. Additional force is then required to overcome the stiction.
A controller in automatic control mode will continue to change its output in an attempt to get the process variable to its set point. While the valve is sticking, the process remains deviated from set point but additional pressure builds up in the valve actuator. If enough pressure has been built up to overcome the static friction, the valve breaks free and travels to the new controller output which is now far beyond its original value. This causes the process to overshoot its set point. Then the valve sticks at the new position, the controller output reverses its direction of travel and the whole process repeats in the opposite direction. This causes an oscillation, called a stick-slip cycle. If loop oscillations are caused by stiction, the controller output’s cycle often resembles a saw-tooth wave, while the process variable may look like a square wave or an irregular sine wave.
Stiction might be caused by an over-tight valve stem seal, by sticky valve internals, by an undersized actuator, or a faulty positioner. Stiction can be detected by placing the controller in manual mode and making small changes (0.5 percent is recommended) in controller output and monitoring the process variable for a resulting change. If the control valve seems to accumulate a few of the controller output changes before the process variable shows movement, it has stiction.
Because of the widespread adoption of positioners for accurately positioning control valves and dampers, one problem that is more common now than a decade ago, is that of positioner overshoot. Positioners are fast feedback controllers mounted on the final control element to measure the valve stem or damper vane position and manipulate the actuator until the desired valve position is achieved. Most positioners can be tuned. Some are tuned too aggressively for the valve or damper they are controlling. This causes the device to overshoot its target position after a change in controller output. The positioner may cause the valve or damper position to hunt around or even oscillate. Sometimes the positioner is simply defective and causes the device to overshoot. If the controller on a fast-responding loop like a flow control loop is tuned aggressively, the combination with positioner overshoot can cause severe oscillations in the control loop. Positioner overshoot can be detected on fast-responding loops by placing the controller in manual and changing the controller output by 2 to 5 percent.
If a level controller drives a valve directly (i.e. no cascade control), and the valve has dead band, the loop will continuously oscillate. Valve dead band will be discussed later. A level control loop will also oscillate if the controller has an internal dead band around the set point. This is sometimes done to prevent the controller from reacting to process noise, but it should not be done on level loops because of the continuous oscillation it causes.
A loop that is tuned too aggressively (overly fast response) can quickly develop oscillations. Step tests should be done on the process to determine the dominant process characteristics: process gain, dead time and time constant. A step test is done by placing the controller into manual mode and changing its output by a few percent (between 2 and 5 percent is normally sufficient). Three or more step tests should be done to compare the results, throw out outliers and use the average.
Proven, broad-spectrum tuning rules like the Cohen-Coon or Lambda tuning rules should be used to calculate new controller settings. However, the Cohen-Coon tuning rules are too aggressive in their original form, and it is recommended to use only half of the calculated controller gain. Best practices prescribe using tuning software for analyzing step-test data and calculating new controller settings.
Interaction between loops with similar dynamics can cause the two loops to “fight” each other. One example of this is the main steam temperature controller interacting with the unit load controller or the steam pressure controller. If the steam temperature is too high, the steam temperature controller injects more spraywater into the desuperheaters. This increases steam production that will either increase unit load or steam pressure, depending on the front-end control mode. In any event, the fuel firing rate will be reduced, the steam temperature cools, and the whole effect reverses and cycles.
Cyclical interaction is aggravated by aggressive tuning. Most boiler control loops are traditionally tuned aggressively to obtain a fast response during load changes and boiler upsets. Because of the highly interactive nature of boiler subsystems, many boilers are prone to cyclical interactions.
To solve problems with cyclical interactions, control loops have to be tuned less aggressively. Using the Lambda method for tuning controllers results in very stable control loops. One can think of highly interactive control loops as a tub filled with water. If you drop a stone in the tub, lots of waves result that take a while to stabilize. Using the Lambda tuning method is like replacing the water with oil. Now if you drop the stone into the tub, the oil just absorbs the disturbance and no persistent wave action results. The pulp and paper industry is also plagued with highly interactive processes, and it has had great success using the Lambda tuning method.
In contrast to oscillations that are periodic, poor control can also make itself evident in large but random deviations between the process variable and set point. These could be measurement noise, process disturbances or rapid set point changes. To understand how a control loop is capable of handling disturbances, we need to look at the speed of response of a control loop.
There are several measurements for loop response; settling time will be used here. Settling time can be defined as the duration of time during which a deviation between set point and process variable is more than 5 percent of the size of the deviation. The settling time of a control loop cannot be infinitely short. If a control loop is tuned sluggishly, it will have a long settling time. If the tuning is improved, the settling time will be reduced, but only up to a point. If the tuning is made any faster, the loop will become cyclical and the settling time will increase.
The minimum settling time of a control loop is determined mostly by the amount of dead time in the process. For a flow loop, the settling time is about three times the dead time, for a temperature loop it’s between three and four dead times, and for a level loop it is about four dead times.
Measurement noise is deviations from set point that change direction very rapidly. The rate at which this happens is so much shorter than the loop settling time that it is impossible for the controller to eliminate noise or even reduce its amplitude. A controller responding aggressively to noise will likely increase the average deviation size. The amplitude of noise can be reduced through filtering the process variable with a first-order lag filter. It is important to note that a filter increases the apparent dead time of a loop and therefore increases its settling time. Filtering should be applied only when needed, and then as little of it as possible.
A process disturbance can push the process variable away from its set point. Disturbances are often the nemesis of good loop performance. As described above, feedback control is limited in how fast it can eliminate the effects of a disturbance and bring the process back to set point. If the disturbance occurs much slower than the settling time of a control loop, feedback control should be able to significantly reduce its amplitude. If not, it may be a problem with the final control element or the tuning of the controller. One should first check for final control element problems before tuning the controller.
Dead band (sometimes called hysteresis), reduces the effectiveness with which a controller can counteract disturbances. Every time the process variable undergoes a disturbance in a different direction from the previous disturbance, the controller output has to traverse the entire dead band before the valve or damper begins moving. Dead band can be detected very reliably with a simple process test consisting of two controller output steps in one direction and one step in the opposite direction with the controller in manual mode. The second and last steps should be the same size. If the process variable does not reach the same level after the first and third steps, it indicates the presence of dead band. Dead band is a mechanical problem and cannot be addressed with tuning.
A control loop may also appear to have sluggish response if the controller output becomes saturated at its upper or lower limit. If the controller output is constrained by a rate-of-change limiter, it also may cause sluggish response regardless of how well the controller is tuned. Alarms can warn of these conditions or historical time-trends of the controller output and process variable can be reviewed to find their presence.
Once the final control element has a clean bill of health, the controller tuning should be reviewed to see if it is perhaps sluggish tuning that reduces the controller’s effectiveness in counteracting disturbances. Controller tuning advice given earlier applies here too. Note that the Lambda tuning method results in stable control loops, but often cause a sluggish response to disturbances, especially on slow temperature loops. Cohen-Coon tuning provides faster disturbance rejection.
Although correct tuning methods can go a long way in minimizing the effects of disturbances, disturbances sometimes happen so rapidly that feedback control alone is unable to reduce their effects to reasonable levels. Realize that feedback control has a limit to the speed of response. Once this limit has been reached, other solutions must be sought to obtain further improvement in performance. It is sad to hear of personnel spending days and even weeks tweaking a control loop that is already at the limit of its performance capability.
If a disturbance occurs faster than the control loop can respond, there is very little the controller can do to reduce its amplitude. In cases like this the feedback controller can be greatly augmented with cascade and feedforward control. Although the standard design of boiler controls includes several cascade and feedforward controls, they have sometimes not been tuned for optimal response.
A good example of the application of cascade control is the main steam temperature controller cascaded with the desuperheater outlet temperature controller. The latter virtually eliminates disturbances coming from changes in spraywater pressure and temperature upsets coming from the first-stage superheater. There are many more examples in standard boiler control configurations, and often a few additional opportunities for improving boiler controls.
Applications of feedforward control include the feedforward from the steam flow measurement to the feedwater flow controller, forming part of the three element drum level control. Another example is the feedforward between the total air flow and the ID Fans.
Some control problems seem to come and go with time—intermittent problems. These problems are more difficult to track down and solve, but it helps to know what causes to look for.
Nonlinear valve Characteristic
Many control valves and most dampers have a nonlinear installed characteristic. This means that the flow characteristic of the device changes depending on how much open it is. If tuning is done with the valve or damper at the one end of its travel, the settings might not work at the other end and could cause oscillations or sluggish behavior. If this is the case, a function generator (X-Y curve) can be placed in the path of the controller output to cancel out the control valve or damper nonlinearity. Boiler control designs often include function generators, but in some cases these have never been calibrated and still contain the original linear curve.
Many boiler subsystems react differently based on unit load, mills in service, soot levels, etc. In many cases the differences in process characteristics are large enough to affect control loop performance. For example, the cooling effect of desuperheater spray flow is much less at high steam flow rates compared to low steam flow rates. These changes in process characteristics often require different tuning settings for optimal control at various operating conditions. However, this is seldom implemented, leaving the control loop with poor response for most of its operating range. On systems with varying process characteristics, controller tuning should be altered automatically based on the operating conditions. This is accomplished quite effectively by implementing gain scheduling. Gain scheduling, as seen in Figure 6, uses the operating condition (like steam flow rate) as an input to one or more function generators to dynamically adjust the controller gain, and sometimes also the integral time, and derivative time if used.
Obtaining robust and properly performing boiler controls can be very challenging. Control loops can perform sub-optimally due to a variety of reasons and controller tuning alone is in many cases not the ultimate remedy for poor control performance. Through a simple but systematic analysis of the control problem, the root cause of poor control can be established and the problem can be resolved or at least minimized in the most effective way.
Author: Jacques F. Smuts, Ph.D., P.E. is the founder and principal consultant of OptiControls Inc. in Houston, Texas. He provides control loop optimization services and training to industrial companies worldwide and has more than 20 years of experience in process control, including seven years in power plants. Jacques has developed controller tuning and loop performance monitoring software that ranks among the top three applications in this class. He has trained hundreds of engineers and technicians in the field of process control and has optimized thousands of control loops.
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