ChE 4162 Control Laboratory Methodologies Fall Control Laboratory Methodologies

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1 Control Laboratory Methodologies Edited by: HJT from Material by DBM 1/11 9/23/2016

2 1. Introduction There seem to be about as many ways to study and tune control systems as there are control engineers. What follows are some of the more common methodologies for carrying out control system studies and tuning traditional controllers. When in doubt, these should be of help, though consulting a good text on the subject must certainly be recommended as well. 2. Typical Starting Tuning Constants for Flow Controllers Flow loops are often too fast (i.e., low lag time) to use the standard methods of analysis and tuning. Controlling flows with a digital control system introduces the additional issue of the control system scan interval. Indeed, the scan interval of the controller may constitute a significant part of delayed response. This scan interval is pure dead time as far as the controller is concerned. Although the speed of most modern control systems make this dead time small, it is large enough compared to the lag of the rest of the system to force a low controller gain in most cases. Typical digital flow control loop tuning constants range as follows: gain from 0.2 to 0.7 %OP/%PV, integral time from 0.05 to 0.3 minutes, no derivative action. Though these ranges of values are typical, it is entirely possible that a particular controller will exhibit better control with values outside these ranges. 3. Typical Starting Tuning Constants for Level Controllers Level Control Strategy and Its Relation to Process and Design For a PID controller, the simplest mode possible would be the use of proportional control only. But is such simple control usable for controlling level in a vessel? Or must we also employ integral (or even derivative) mode to accomplish successful control? Well, that depends on the purpose of the vessel in question, the reasons for needing or wanting to control level, and the design of the vessel level management system. Is the vessel serving as a wide spot in the line a surge vessel, intermediate storage, or condenser accumulator designed to filter out upstream flow variations from downstream processes? Is the vessel large or small in comparison to the flow into and out of the tank? Is the vessel serving some important process function where level might strongly influence performance, such as a reactor, a reboiler, or an evaporator? Does the design of the vessel level management system result in a self-regulating level process or an integrating process? Once these questions have been answered, the choice of level control strategy [i.e., selection of PID modes and aggressiveness of tuning values (tight versus averaging control)] becomes more obvious. Self-regulating level processes are tackled by traditional methods, but Marlin gives useful guidance for approaching the classical situation of an unstable level process Edited by: HJT from Material by DBM 2/11 9/23/2016

3 i.e., an integrating process (Marlin, 2000): Tight Level Control Proportional Only Proportional + Integral Fmax F Kc Kc L L K c F max max is the controller gain, anticipated change in the uncontrolled flow, L max one-sided level change allowed ( 5% of maximum range) K c F max max max is the controller gain, anticipated change in the uncontrolled flow, L max one-sided level change allowed ( 5% of maximum range) T i 2 4 A Ti K is the controller integral time, is the damping coefficient, set to 1, and is the cross-sectional area of the vessel Averaging Level Control Proportional Only Proportional + Integral Fmax Fmax Kc Kc L L K c max is the controller gain, F max anticipated change in the uncontrolled flow, L max one-sided level change allowed ( 40% of maximum range) A K c c max is the controller gain, F max anticipated change in the uncontrolled flow, L max one-sided level change allowed ( 40% of maximum range) 2 4 A Ti K c i T is the controller integral time, is the damping coefficient, set to 1, and A is the cross-sectional area of the vessel Edited by: HJT from Material by DBM 3/11 9/23/2016

4 4. Tuning PI Controllers by The Trial-and-Error Method Flow control loops are PI controllers particularly amenable to what is called trial-anderror tuning. Trial-and-error tuning is no more than the following notions rolled into a method: Start with some crude estimate of reasonable tuning constants by consulting the literature or experience (yours or others). For flow controllers, see Section 2 of this document. A good rule of thumb would be to start with the low end of the gain range and the high end of the integral time range. These values would be the least aggressive of the recommended ranges. Put the controller in AUTO and establish control at a setpoint of interest then increase (or decrease) the setpoint by a fixed amount. This is called a closed loop step test. The response of the controller to the closed loop step test can be compared to the tuning map in Figure 1. A fast, but stable response is traditionally characterized as the Desired Response shown in the center graphic of this figure. If the response is more readily characterized by one of the other graphics, it would be considered suboptimal. First, change the integral time to move toward the Desired Response by doubling or halving the integral time, I. Finally, change the gain, Kc, to move toward the Desired Response by doubling or halving it. Lesser changes in these tuning constants may improve the response even further, but very small changes in these parameters will be seen to have little or no effect on the apparent response. Edited by: HJT from Material by DBM 4/11 9/23/2016

5 2.0 K c K c Desired Response 0.5 K c 0.5 I I 2.0 I Figure 1. Tuning Map for Trial and Error Tuning of PI Controllers. All Plots are Response of PV vs Time after a Step Change in SP. 5. General Tuning Tips 1 Tune Course, Not Fine Realizing that the performance of a feedback controller is not sensitive to the precise adjustment of its tuning parameters significantly simplifies the tuning task. Faced with the infinite possible combinations of precise tuning parameter values, you might give up the task of tuning before you even get started. But once you realize that the controller performance does not require tuning parameters to be set precisely, you reduce the number of significantly different combinations to a workable number. Moreover, you will be satisfied by the large improvements in performance that can be achieved by coarse tuning. in sharp contrast to the frustration you will feel in the small incremental improvements achieved through fine tuning. How coarse is coarse tuning? When tuning a controller, seldom change a parameter by less than half its current value. 1 Corripio, A.B., Tuning of Industrial Control Systems, 2 nd ed., ISA, 2001, pp Edited by: HJT from Material by DBM 5/11 9/23/2016

6 Tune with Confidence One of the reasons controller performance is not sensitive to precise tuning settings is that any of the parameters may be adjusted to make up for non-optimal values in the other parameters. One effective approach is to select the integral time first and set the derivative time to about one-fourth of the integral time, or, if the dead time is known, to one half the dead time. Then adjust the proportional gain to obtain tight control of the controlled variable without undue variations in the manipulated variable. If the response is still too oscillatory, cut the integral and derivative times in half; then readjust the gain. When you obtain satisfactory performance, leave it alone. Do not try to fine-tune further. If you try to fine-tune it you will be disappointed by the insignificant incremental improvement. 6. Tuning of Digital PID Controllers Special Considerations Scan rates in digital control systems can vary from a fraction of a second to several minutes. If the scan rate of the digital control system, T, is small relative to the process response time, then controller settings suitable for a continuous controller can also be utilized in a digital PID controller allowing the use of a wide variety of algorithms for estimating tuning constants. However, this delay waiting for the next scan is best taken into account when using tuning formulas. It is relatively common practice in tuning digital PID controllers to add T/2 to the process time delay, to, before using typical controller tuning relations. 7. Tuning by The IAE Minimum Error Method For more information, see Performance Monitoring Fundamentals by Rice et al. or Smith and Corripio, Chapter Tuning by The ITAE Minimum Error Method For more information, see Performance Monitoring Fundamentals by Rice et al. or Smith and Corripio, Chapter Tuning of Cascade Control Systems Romagnoli and Palazoglu [2, pp ] recommend a simple two-step procedure for tuning a cascade control system: Step 1: Determine the settings for the secondary loop controller using any technique. Step 2: Using these settings, place the secondary loop in automatic and determine the settings for the primary loop controller, again by any technique. Edited by: HJT from Material by DBM 6/11 9/23/2016

7 10. Identifying Nonlinear Process Behavior and Consequences Nonlinear Equipment Behavior It would be naïve to assume that every piece of equipment behaves linearly throughout its full operating range. Equipment such as control valves, electrical heaters, and pumps exhibit operating curves many of which are, indeed, curves and not straight lines. The most straightforward way to identify and quantitatively assess the response of a piece of equipment is to put it through its paces and plot the response (or some function of the response). Alternatively, manufacturers often supply operating curves for the equipment they manufacture. In the case of a control valve, instead of using the term operating curve, we refer to its response as its inherent valve characteristics. Smith and Corripio cover identification of control valve characteristics extensively. Nonlinear Process Behavior If one puts many pieces of equipment together into a process along with the various transport phenomena also exhibited by that process, one might expect that over the full range of operation of that process its response might not be linear. Indeed, if any of the individual components exhibit nonlinear behavior, it can affect the overall process. Thus, when operating conditions change, the process behavior may change as well. We refer to such processes as nonlinear processes. With nonlinear processes, process gains and time constants change when process conditions change. Thus one cannot speak in terms of identifying a single value for the process gain or time constant. If the process gain and/or time constant are changing with changing operating conditions, then to expect that one particular set of tuning constants would yield optimal response over the full range of operating conditions might be naïve as well. However, we realize from other considerations that modest changes in tuning constants do not produce significant changes in system response. It is then plausible to expect that only processes that exhibit significant nonlinearity might require noticeable changes in tuning to affect the desired response. This would be reasonably straightforward to test: Simply tune a particular controller to exhibit the desired response at the center of the operating range using closed loop step testing and a quantitative criterion (e.g., IAE, ITAE, etc.). Then use the resulting tuning constants to run closed loop step tests nearer the extremes and measure the degree of deviation from the best response at the center of the operating range. For more meaningful results but by a more time-consuming process, one could optimally tune the controller at the center and near the extremes and compare the resulting tuning constants. A significant difference in either case would be indicative of substantive process nonlinearity. Edited by: HJT from Material by DBM 7/11 9/23/2016

8 11. Conducting a Closed Loop (Setpoint) Step Test To determine the goodness of the dynamic response of a controller using a particular set of tuning constants, one first conducts a closed loop step test. Here s a simple procedure for conducting such a test: Put the controller-of-interest in AUTO (or CASCADE, if the controller is a primary controller of a two-controller cascade strategy). Put all other controllers which could potential interact with the controller-ofinterest in either AUTO or MANUAL as appropriate to endeavor to keep their process variables from changing during the test. Adjust the controller-of-interest s setpoint to the desired start value for the step test. This value would typically be just above or below that point in its range for which good control is being sought. For example, if one is trying to determine the response of a controller near its 50% point, then one would adjust the starting setpoint to 47-48% or perhaps 52-53%, depending on whether the step test was being performed by using an upward or a downward step. Apply a step change in the controller-of-interest s setpoint. This step change could be in the upward direction or the downward direction. The size of a typical step change could be from 3 to 5%. (Very large changes can lead to instability if tuning constants are not suitable and could also raise the complexity of response due to nonlinearities incurred over large ranges.) Record the response information for as long as is needed by the technique used to determine desired response or until steady state at the new setpoint is reached. For more information, see Performance Monitoring Fundamentals by Rice et al. 12. Closed Loop Tuning by Ziegler-Nichols Method This method, also known as the ultimate-gain method, involves pushing the response of the system until it continuously cycles. See Romagnoli and Palazoglu [2, pg. 195 ff.] for more information. 13. Testing Controller Interactions In systems with only a single controlled variable, the concept of interaction has no meaning. In many actual processes, however, there are often multiple control objectives. For instance, a hot water supply tank might have controllers to hold both tank level and effluent temperature at desired values. A blending system might have controllers to adjust composition and flow rate. A complex stirred tank reactor might have controllers to maintain level, temperature, and flow rate. In such systems it is distinctly possible that efforts to control one variable might Edited by: HJT from Material by DBM 8/11 9/23/2016

9 represent a disturbance to systems controlling other variables a situation known as loop interaction. If these loop interactions are significant, overall system stability could become a serious issue. Existing process systems which have not been expressly designed to reduce or eliminate the effects of such loop interactions may exhibit such instability, despite the fact that each individual control loop had been previously tuned for optimal and stable response. Presuming that safety considerations allow for it, the best way to identify the existence of loop interactions is to put all loops in service as intended and test the entire system for its response to reasonable setpoint and disturbance (or load) changes. Departure from the ideal individual loop response identified previously will be obvious in cases where significant loop interaction is present even to the point of creating instability or the inability to control all variables in response to change. If significant loop interaction is identified in an existing system, what can be done about the situation? Short of redesigning the control system, detuning the individual controllers is the best shot at improving control and reducing instability. To detune a controller, one simply reduces the gain of the controller, making its response to error less aggressive. But with multiple control loops in the system, which ones of these should be detuned? Here are a few guidelines: Detune the least important controller(s), or Detune the controller(s) with the highest gains, or Use trial-and-error to decide which controller(s) should be detuned. 14. Process Step Testing for Open Loop Process Characterization One of the earliest techniques for developing settings for automatic controllers is the Ziegler-Nichols open loop process characterization method, refined by Cohen and Coon. This method produces a process reaction curve from which important information about the process in question can be gleaned. A short treatise on open loop testing for tuning purposes is the document from APCO, Inc., or consult Romagnoli and Palazoglu [2, 194 ff.] When conducting process step tests in the Ziegler-Nichols tradition (or by more sophisticated means), one must generate good process test data. A number of qualifying questions must be asked and answered by the testing method and analysis techniques. See Model-Based Tuning Methods by Arbogast et al for an explanation of these. Smith and Corripio present this information as well. Edited by: HJT from Material by DBM 9/11 9/23/2016

10 15. Getting Starting Tuning Constants from Open Loop Step Test Results There are numerous techniques for developing good starting tuning constants from open loop step test results. Indeed, Ziegler and Nichols had their own recommendations, to be followed by countless others, each of whom believed their method to be an improvement of sorts. The Ziegler-Nichols rules and some others are described in the attached document: Open Loop Tuning Rules by Advanced Process Control and Optimization. Another technique for developing tuning constants from open loop test Internal Model Control is described in Model-Based Tuning Methods by Arbogast et al. Smith and Corripio also present this information well. 16. Relevant Literature References 1. Perry, R. H. and D. W. Green, eds. Perry s Chemical Engineers Handbook, 7 th Ed. New York: McGraw-Hill, Romagnoli, J.A., A. Palazoglu. Introduction to Process Control, CRC Press Taylor and Francis Group, Seborg, D. E., T. E. Edgar and D. A. Mellichamp. Process Dynamics and Control, 2 nd Ed. New York: John Wiley and Sons, Smith, C.A. and Corripio, A.B., Principles and Practice of Automatic Process Control, 2 nd Edition, John Wiley and Sons, New York (1997). 5. Smith, C.L., Process Engineers: Take Control! Chem. Eng. Prog., August, 2000, Rice, R.C., R.R. Jyringi and D.J. Cooper. Performance Monitoring Fundamentals: Demystifying performance assessment techniques, Monitoring Fundamentals-2.pdf, (Last accessed: 1/27/16). 7. VanDoren, V.J., Loop Tuning Fundamentals Control Engineering, July 1, 2003, (Last accessed: 1/27/16). 8. Arbogast, J., D.J. Cooper and R.C. Rice. Model-Based Tuning Methods for PID Controllers, Tuning Methods for PID Controllers- 2.pdf, (Last accessed: 1/27/16). 9. APCO, Open Loop Tuning Rules, (Last accessed: 1/26/16). 10. Marlin, T. E., Process Control: Designing Processes and Control Systems for Dynamic Performance, 2 nd Edition, McGraw-Hill, (Slides for classroom use based on this text can be found at (Last accessed: 1/27/2016) Edited by: HJT from Material by DBM 10/11 9/23/2016

11 Some notes on possible revisions needed for this document: New link for APCO: Some notes from my blackboard, apparently on a process control practicum: What do each of the modes (P, I and D) do? The loop: open and closed Cascade Direct and reverse HW tuning constants IMC SP vs load testing Open-loop testing Closed-loop testing Filtering Ideal PID versus? Noise Valve characteristics Nonlinearity Interaction Performance criteria (integral, settling time, overshoot, decay ratio, etc.) Process models Process gain, time constant and dead time Self-regulating versus integrating Stability Hand/manual techniques versus Loop-Pro regression technique. Edited by: HJT from Material by DBM 11/11 9/23/2016

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