Tuning the Control Loop

For noninteracting control loops with zero dead time, the integral setting (minutes per repeat) is about 50% and the derivative, about 18% of the period of oscillation (P). As dead time rises, these percentages drop. If the dead time reaches 50% of the time constant, I = 40%, D = 16%, and if dead time equals the time constant, I = 33% and D = 13%. When tuning the feedforward control loops, one has to separately consider the steady-state portion of the heat transfer process (flow times temperature difference) and its dynamic compensation. The dynamic compensation of the steady-state model by a lead/lag element is necessary, because the response is not instantaneous but affected by both the dead time and the time constant of the process. 

Post-Oil Energy Technology After the Age of Fossil Fuels 

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Tuning the control loop for the liquid level in the bottom of a distillation column can be similar to a reflux drum. However,... 

Once you have completed the steady-state design, use the results to size all the valves in the system. Add the control loops (two) determined from the steady-state design, with the set points which allow the control objectives to be met at all times. Inventory and pressure control will be available automatically, but you can add your own control loops if desired. Solve the column in dynamic mode. You are free to change set points or create disturbances to the system to examine how the control system performs. A good starting point would be to see how the column responds to step disturbances in the feed between the expected feed cases. You may need to tune the control loops in order to produce an adequate response. 

The cost functional is the indicator of how well the control loop is functioning. The lAE criterion used essentially says "measure the cumulative difference between the actual value and the desired set point" this cumulative score is a measure of control system performance. With this code in the model, the commands to do the controller tuning are ... 

After proper pairing of manipulated and controlled variables, we still have to design and tune the controllers. The simplest approach is to tune each loop individually and conservatively while the other loop is in manual mode. At a more sophisticated level, we may try to decouple the loops mathematically into two non-interacting SISO systems with which we can apply single loop tuning procedures. Several examples applicable to a 2 x 2 system are offered here. 

In tuning the controller we assume that three small lags rm exist in the loop, so the controller sees the total openloop transfer function ... 

Filters are likely to be more effective on fast processes and can complicate or limit the response of a PID controller. One way to compensate for this is to tune the controller with the filter inserted into the feedback loop. 

Set point and load changes affect the behavior of the control loop quite differently, owing to the dynamics in their path. There are no dynamics involved with changing the set point, unless intentionally placed there for purposes of filtering the set point. However, there are always dynamics in the load path. A controller tuned to follow set point changes tends to respond sluggishly to load variations, and a controller tuned to correct for load disturbances tends to overshoot when its set point is changed. 

The cause of control loop cycling can be that they interact with other loops. When loops interact, it is necessary to make sure that their response speeds are not even similar. To avoid oscillation, one should select response speeds that differ by a factor of 3 to 10, depending on the degree of interaction. In tuning interacting loops, one would place the downstream loops in manual while tuning the "upstream loop," and once the upstream loop s speed of response is determined, use a multiple of that to set the downstream controllers. 

The next part involves controller tuning. We must determine the tuning constants for the controllers in the plant. While this task is often performed by using heuristics and experience, it can sometimes be a nontrivial exercise for certain loops. We recommend using a relay-feedback test that determines the ultimate gain and period for the control loop, from which controller settings can be calculated (Luyben and Luyben, 1997). 

We briefly discussed in Chap. 2 (Sec. 2.5) one important aspect of distillation column control when one of the products leaving the column is a recycle stream. Should the composition of this recycle stream be controlled Probably not from the perspective of the isolated column because the control loop holding the composition of the other stream leaving the column, which is a product from the plant, could then be more tightly tuned. However, the plantwide control perspective may show that the performance of the reactor can be improved by holding the purity of the recycle stream more constant, and this could result in smaller disturbances to the column. The overall effect may be better product quality control even though the product-quality loop is less tightly tuned. 

These nonlinearities are the main reason an operating margin must be considered when tuning the controller. If the loop is to be robust and operate in a stable manner over a wide range of conditions, conservative values of the tuning parameters must be chosen. Unfortunately, this results in poorer performance under most conditions. One technique to handle known nonlinearities is to provide tuning parameters that vary based on measured process conditions. 

G.K. McMillan, Tuning and Control Loop Performance, 3rd ed., Research Triangle Park ISA—The Instrument Society of America, 1994. 

The first problem which was evident during startup was that the steam reformer steam to carbon controls (S/C) were inherently unstable and that minor upsets would lead to plant trips due to low s/c ratio. While the control system is complex, by. modelling using dynamic simulation, it was easy to determine that one of the control loops was unstable due to the discrete sampling nature of the control system and the chosen tuning parameters. (Ref 2)... 

The control computer/DCS system consists of controllers, A/D and D/A converters, and the signal conditioifing hardware and software, i.e., filtering and validation. Each of these components requires separate evaluation. Table 15.5 lists possible problems with the controller/DCS system. One way to initially check controller tuning is to place the control loop in manual (open the control loop) and observe whether the controlled variable lines out to a steady-state or near steady-state value. Comparing the open-loop and closed-loop performance indicates whether the controller is upsetting the process. If not, disturbances to the control loop in question are the primary source of the upsets. 

Simultaneously satisfying each of these objectives is never possible therefore, tuning is a compromise. For example, tuning for minimum deviation from setpoint for normal disturbances is contrary to tuning the controller to remain stable for major disturbances. That is, if the controller is tuned for normal disturbances, the closed-loop system may go unstable when a major disturbance enters the process. On the other hand, if the controller is tuned for the largest possible disturbance, control performance is likely to be excessively sluggish for normal disturbance levels. 

After F has been adjusted to tune the set of decentralized PI controllers, fine tuning of the controller settings should be used. For example, if one observes that one of the control loops is slow to settle at setpoint in a manner similar to Figure 15.3a, an increase in integral action for that loop should be tested. If one of the loops exhibits ringing, derivative action should be tested to determine if it improves the feedback control performance of that loop. In the latter case, derivative action should be tuned in the manner that was described in Section 15.3. 

How do you understand the opening of the control loop shown in Figure 16.7 Explain in practical terms how one tunes a feedback controller for an existing process in a chemical plant. 

What is the objective of the adaptation procedure Clearly it is not to keep the controlled variable at the specified set point. This will be accomplished by the control loop, however badly. We need an additional criterion, an objective function that will guide the adaptation mechanism to the best adjustment of the controller parameters. To phrase it differently, we need a criterion to guide the adaptive tuning of the controller. Any of the performance criteria we discussed in Chapters 16 and 18 could be used ... 

Figure 16.3 demonstrates, in a qualitative manner, the shape of the expected closed-loop responses. When we tune the controller parameters using ISE, IAE, or ITAE performance criteria, we should remember the following two points ... 

After introducing the necessary decouplers, can you tune the controllers of two loops separately so that the stability of the overall process is guaranteed (Hint Examine closely the closed-loop characteristic equations of two decoupled loops.)... 

Temperature. The process temperature of an evaporative crystallizer may be controlled by the absolute pressure in the vessel. Direct flow control of steam to an ejector, while economizing on steam utilization, is subject to pressure fluctuations due to disturbances in the steam supply pressure. Applying cascaded flow control would decrease the response time of the control loop. Flow control of a bleed gas or exhausted gas into the suction of the vacuum source is the most responsive and precise control option. Critically damped tuning of the pressure control loop should be implemented to prevent rapid temperature changes and high supersaturation generation from fast swings in pressure. 

The previous step gives an indication of the controller tuning the closed-loop disturbance gain (CLDG) should be smaller than the loop transfer function [l+g,v(s), (s)] for each disturbance, where g (s) is the open-loop input/output transfer function, and, (s) the controller model. 

Closed loop simulation. Here the first task consists of implementing and tuning the controllers. The use of prescribed local control structures, or setting perfect control for fast loops simplifies this task and preserves the plantwide character of the analysis. Here... 

These observations are verified by closed-loop simulations. The RGA for the matrix P 0, with X.n = 1.7, indicates that the control loops can operate in a stable fashion only by pairing the inputs and outputs diagonally. Thus, diagonally paired PI controllers are tuned according to the improved IMC-based tuning rules (see Section 21.4), with Kq = 0.29, = 64 min, Kcz = 0.42 and = 67 min. As shown in... 

EXAMPLE 21.10 Tuning PI Control Loops for the Shell Process (Example 21.6 Revisited)... 

Reduction of variability can be accomplished by the selection of the best control strategy for the distillation column to shed disturbances and by tuning process control loops for... 

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