6 Steps to Improved PID TuningWHITE PAPER

6 Steps to Improved PID Tuning

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Table of ContentsIntroduction 3

Defining Terms 4

A Well-Tuned Loop 5

6 Steps to Success 7

Application Example 17

Conclusion 19

About the Authors 20

ü Improved PID tuning directly affects the bottom line

ü Simple six-step process provides rapid return-on-investment

ü Learn from an actual case study example

KEY TAKEAWAYS

ü Understand what a well-tuned loop is

ü Rapidly identify and correct problems

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IntroductionPart 1

Underperforming PID controllers can cripple plant profitability. An intuitive six-step approach to PID tuning results in improved throughput and yield, minimized energy consumption and less defects.

Common sense informs us that doing anything “out of control” is a bad idea. Conversely, operating “in control” represents a far better situation, but what exactly does that mean, particularly in the context of an automated industrial continuous process? Typically, these types of processes involve flow, pressure, temperature, level and other measurable and controllable variables. Most process plant operators need careful control of these values to ensure setpoint changes are rapidly followed and process disturbances are quickly accommodated.

For continuous process plants, Proportional-Integral-Derivative (PID) loop controllers are a primary method of achieving this control. Unfortunately, many PID loops can be difficult to tune properly and downright unintuitive. Manual tuning efforts are time-consuming and offer little proof that an optimum solution has been reached. Some estimates indicate that more than 65% of process PID loops are underperforming with up to 30% operating in manual mode; both of these conditions result in reduced process stability, product variations, increased energy expenses and additional equipment wear.

Significant mathematical theory exists behind PID algorithms, but users need a straightforward way to achieve real-world results. There is a repeatable path to obtain optimum PID controller tuning so processes can operate at best efficiency. By following an intuitive six-step approach that is implemented with the right software tools, users can reliably tune and document control loops. This white paper details a practical PID tuning methodology to reach these goals.

Underperforming PID controllers can cripple plant profitability. An intuitive six-step approach to PID tuning results in improved throughput and yield, along with minimized energy consumption and defects.

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The following is a list of some useful terms and abbreviations used throughout this white paper:

Process Variable (PV): the process signal for which control is desiredSetpoint (SP): The target for the PV, usually provided by operations staff but may be programmatically generated is sometimes called setpoint variable (SV)Control Output (CO): The result of the PID calculation, which becomes the output to command a field device that can affect the PV is sometimes called the manipulated variable (MV)Error: the difference between the PV and the SPDead time: the time it takes for the PV to respond to a change of the CODirect acting: Increasing the CO increases the PV (see also reverse acting)Reverse acting: Increasing the CO decreases the PV (see also direct acting)Self-regulating (also known as non-integrating) process: In any system, like a heat exchanger, where if all inputs and outputs are held constant, the process will seek a steady-stateNon-self-regulating (also known as integrating) process: any system, like a surge tank level, where the process can only reach steady-state at a certain balancing point and will otherwise continuously increase or decreaseProcess gain: how far the PV moves for a change in the CO (for self-regulating processes), or how far and how fast the PV moves for a change in the CO (for non-self-regulating processes)Process time constant: how fast the PV reaches 63% of its total change (for self-regulating processes)Process dead time: how long it takes from when the CO changes until the PV first movesArrest time: The time to recover from a major disturbance; should be far less (perhaps half) of the time to reach an alarm condition

Defining Terms

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A Well-Tuned Loop

A well-controlled process has less variability in the measured PV, allowing operators to reliably control the process closer to the maximum profit constraint while minimizing inefficiencies. The trend in Figure 1 contrasts the action of a PID control loop when it is poorly tuned vs. well-tuned.

Figure 1 – For almost all measurable and controllable continuous industrial processes, there is an optimal setpoint (SP) that the measured process variable (PV) should follow as closely as possible

For the time period from zero to 60 minutes, the PV and the OC are oscillating wildly about the SP. This unstable operation causes unnecessary equipment cycling, wastes energy, and may result in substandard product. In addition, because of the PV swings, the SP must be set well below the constraint to provide a safety margin, so the process stays below the constraint zone. This constraint could relate to equipment limits, ruined product, or similar conditions which must be prevented.

After successfully being tuned, the PID loop from 60 to 120 minutes provides a very stable PV centered around the SP, with the CO responding quickly to correct error without overshooting. In fact, the process becomes so stable the operators can adjust the SP to run much closer to the constraint, as shown from the 120- to 180-minute time period.

6 Steps to Improved PID Tuning

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Determining the best tuning parameters for a PID loop isn’t always an easy process, but optimal tuning is an excellent way to improve process quality, productivity, efficiency and safety. Many detailed papers have been written to explore the complex math behind PID algorithms and there are also numerous theory-based approaches to loop tuning. This classical approach requires extensive data collection and the development of detailed mathematical models. Completely manual loop tuning approaches are an alternative, but are very dependent on individual skill and experience, and are thus hit or miss.

A better solution is advanced loop tuning software, especially when integrated with an automation platform. This approach saves time and makes loop tuning accessible and repeatable across a wide range of process plant personnel. Users can focus on basic system operations and triggering some simple reactions, while the software does the heavy lifting of gathering data, fitting it to a model and then determining best results.

Even when leveraging loop tuning software, it is helpful for users to have a basic understanding of PID loops. Following are the three PID loop parameters, along with a description of each:

• Proportional Parameter: Associated with “how far” the PV has moved away from the desired SP. The difference between PV and SP is the error, and the “P” term determines the magnitude with which this error will impact the CO response.

• Integral Parameter: Associated with “how long” the PV has been away from SP. The integral term integrates or continually sums up error over time. As a result, even a small but persistent error will aggregate to a considerable impact on the CO response over time if the “I” term is used.

• Derivative Parameter: Associated with “how fast” the error value changes at an instant in time. The derivative computation yields a rate of change (or slope) of the error curve. This “D” term can be useful for some kinds of loops but is often not needed.

An end user who knows how to operate and control a process, and who understands some basics about PID parameters, can successfully follow a six-step process to successfully tune PID loops.

An end user who knows how to operate and control a process, and who understands some basics about PID parameters, can successfully follow a six-step process to successfully tune PID loops.

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6 Steps to SuccessPart 2

Several approaches to PID controller tuning are possible:

• Manual tuning• Mathematical modeling and calculations• Pushbutton auto-tune• Dedicated PID tuning/modeling software

Manual tuning is a very seat-of-the-pants approach, and in the hands of a qualified and patient technician may return acceptable if not ideal results, especially for the simplest of loops. However, it takes a long time to implement, and it is difficult to prove if loop tuning is optimized.

Mathematical modeling is a more rigorous approach but presupposes that the user can create a proper mathematical model. This is not usually practical for most systems. There are also some approaches where basic tests can be performed and observed, with the results used to calculate the PID tuning parameters. Examples are the Ziegler-Nichols tuning methods for open- and closed-loop tuning. These can be helpful but are not suitable for all conditions.

Instead, most users now opt for controllers or platforms which include some form of software-based tuning solution. Many controllers offer an on-board auto-tune feature, which when carefully used can return good results for most PID loops. Taking this a step further, dedicated PID turning and modeling software can deliver a much more robust solution.

Regardless of the process type, the most advanced tuning software can support multiple model fits without editing raw data. Various types of bump tests are possible, and steady-state conditions are not required prior to the test.

... most users now opt for controllers or platforms which include some form of software-based tuning solution.

6 Steps to Improved PID Tuning

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Dedicated PID tuning software is not a self-contained one-button solution. For best usability it must be integrated with the process control platform and used by knowledgeable technicians and engineers. The most thorough way to reliably tune PID loops is to use a six-step approach (Figure 2) as described in the subsequent sections.

Figure 2 – The procedure for designing and tuning PID control strategies can be distilled into these six steps

STEP 1: IDENTIFY THE LOOPS AND OBJECTIVES

The first step is to simply find which PID loops require tuning. This may not be obvious because poorly tuned systems can run for a long time without drastic or easily spotted consequences. A reactive approach may follow once operators submit work orders about a badly behaving system, such as one that oscillates or exceeds alarm limits.

Proactive users can monitor, historize and analyze key performance data as a means of identifying underperforming systems. Problems may be evidenced by large numbers of alarm excursions, persistent deviation conditions, off-specification product, or controlled equipment operating at greater extremes than historically normal.

Find Step Model Tune Test DocumentIdentify the

controller and specify the

design level of operation (DLO)

and control objective

Perform a “Bump Test” and collect

dynamic process data

Fit a model to the process

data

Use tuning correlations to calculate

tunings based on model

Implement and test results

Document the tuning process

Proactive users can monitor, historize and analyze key performance data as a means of identifying underperforming systems.

6 Steps to Improved PID Tuning

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A proactive approach includes investigating whether the root cause is due to mechanical problems, process conditions, PID controller tuning, or a combination of these factors. A proper examination will detect these problems and develop a corrective action plan, which must specify the final control objective. The acronym “SIMPLE” offers suggestions to defining good control:

S Safety: Design the control objective to prevent loop failure and maximize safety.

I Impact: Consider where the control loop fits within the overall process, along with any associated upstream or downstream impacts and disturbances.

M Management: Match management’s performance requirements, or document why they cannot be achieved.

P Profit: Include economic factors (product costs, energy, equipment wear).

L Longevity: Keep the control strategy as simple as possible, which promotes robust longevity.

E Equipment: Process equipment assets are expensive to buy, operate, and maintain, so they should be protected to the greatest extent possible.

There may be several related primary control objectives for any given loop. Consider liquid level control within a reflux drum (Figure 3).

Figure 3 – Step 1 is evaluating the process configuration and determining the control objectives

6 Steps to Improved PID Tuning

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One primary goal is to maintain the liquid level PV at the assigned SP. But beyond this, it is important for the level to maintain consistency, which promotes distillation column stability and efficiency. A third constraint is preventing an environmental release by staying below the drum high-level limit. For this example, the level indicating control (LIC) interacts with two downstream flow indicating control (FIC) loops. The trend indicates how tuning that is too aggressive (too fast of a response to setpoint changes) not only fails to control the reflux drum properly, but ripples through to negatively impact downstream loops.

Once the PID loop targeted for tuning has been identified and a control objective specified, the next task is to gather data about the loop by manually manipulating the process.

STEP 2: STEP OR BUMP THE PROCESS

The point of gathering data is to obtain enough information to reveal the dynamic behavior of the process system, define its characteristics, and describe how the PV responds in relation to changes in the CO. Basically, the data must show the cause and effect correlation for the physical process.

Once this is done, a bump test can be performed, which consists of changing the CO far enough and fast enough to trigger an observable PV response that clearly dominates any random noise. An ideal response is at least five times the magnitude of the noise.

6 Steps to Improved PID Tuning

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Two testing methods are possible. The preferred method is open-loop with the PID controller in manual mode so the CO can be adjusted directly, but it is also possible to use a closed-loop method in automatic mode where the SP is adjusted to create the response. For either method, the three common forms of a bump test are:

• Step: CO is stepped from one constant value to another• Pulse: Essentially two consecutive steps; first stepping from one

constant value to another, then returning to the original constant• Doublet: Three consecutive steps; first stepping from one constant

value to another, then stepping back past the original constant, and finally stepping to the original constant

Figure 4 shows several forms of open-loop and closed-loop tests, where the CO is changed while the PV response is monitored. Data sampling rates are an important aspect of testing to assure there is sufficient resolution for calculations. Data should be collected at rates at least 10 times faster than the process time constant. Some guidelines for recommended sample rates based on process types are:

• Flow, Pressure: Less than 2 seconds preferred• Level: Between 1 and 5 seconds based on tank size

(faster sampling for smaller tanks)• Temperature, Fast: Between 5 and 15 seconds• Temperature, Slow: Between 15 and 30 seconds• pH, Concentration: Between 5 and 30 seconds

Figure 4 – Step 2 is to step or bump the control output (CO)

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There are several other situations and challenges which can affect feedback signals, and each must be considered to ensure the test data is suitable, with true system dynamics not masked. These situations are:

• Steady state: Beginning with the process at steady state (neither increasing nor decreasing) helps ensure the results are not compromised by non-test-related dynamics already under way.

• Noise: Testing should exercise the system such that the PV response is observed to be at least 5 to 10 times greater than signal noise, making the data easier to analyze.

• Disturbances: Testing personnel should monitor the process systems associated with the PV being tested to ensure no disturbances outside of the test actions (such as other equipment changing operation, product changes, etc.) are affecting the data.

• Underlying problems: Testing personnel should also look for other problems such as valves that are sticking or have large hysteresis as these will often return bad data.

• Varying process ranges: A subtle consideration is that some processes exhibit different behaviors depending on what part of the process range they are operating in, so one should always perform testing over the entire operating range.

With good data captured, the next step is to model the process.

6 Steps to Improved PID Tuning

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STEP 3: MODEL THE PROCESS

Tuning software must use the captured data to create a mathematical process model, and the tuning can only be as good as the model. Users should understand a few physical basics about the process to facilitate this step, and they may need to enter this information into the tuning software.

There are two types of process action. For direct acting processes, increasing the CO will increase the PV. Think of a modulating steam valve on a heat exchanger, where opening the valve increases the temperature. Reverse acting processes are the opposite, such as a modulating discharge valve on a surge tank where opening the valve decreases the level.

There are also are two types of process behavior (Figure 5). For self-regulating (also known as non-integrating) processes, when all conditions are held steady, the PV will seek a steady state. This is the behavior of a heat exchanger.

On the other hand, non-self-regulating (also known as integrating) processes only reach steady state at exactly one balance point, but will otherwise continually move up or down even if all conditions are held steady. This is the behavior of a surge tank which is constantly filled but will experience a rising or falling level unless the discharge valve is at exactly the right balance point.

Since this white paper is based on using tuning software which performs the heavy calculations, the math underpinning PID calculations will not be explored.

Figure 5 – Step 3 involves fitting a process model based on whether the system is self-regulating (will always settle to a steady-state) or non-self-regulating (has only one possible steady-state, will move up or down otherwise)

6 Steps to Improved PID Tuning

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STEP 4: TUNE THE PID LOOP

So far, the steps have focused on understanding the tuning objective, obtaining data, and fitting a model. Now the focus changes to the actual PID tuning parameters. While basic loops may be roughly tuned by hand, specialized tuning software gives users many advantages by handling all of the math and display options. This type of software can examine many models and data sets quickly, both online and offline, and can even be used to overcome data problems, such as tests not starting at steady state.

For best results and ease of use, the tuning software should be tightly integrated with the user’s control platform. This makes data acquisition easier, and it ensures all equivalent calculations, terminology, and variables are used by both the tuning software and the control platform. In the best case, the tuning software integrates seamlessly with the control platform, and can even be accessed through native menus and faceplates.

PID loops can be tuned aggressively (fast), moderately, or conservatively (slow) (Figure 6). Also, not every process needs to use every element of PID control. For some processes there is a choice between using basic P only, or PI, or full PID control. Software tuning solutions can provide tuning maps showing all the options so the user can choose the best option.

Some processes, such as surge tanks, may only require conservative tuning adequate to prevent over or under filling. Other processes, such as critical product heating, might need very aggressive control to maintain quality.

Figure 6 – Step 4 uses software to analyze the test data and calculate optimal tuning parameters for various levels of responsiveness

6 Steps to Improved PID Tuning

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Figure 7 – Step 5 is crucial for testing the calculated tuning parameters to ensure the PID control responds properly to setpoint changes, load changes, and disturbances

STEP 5: TEST BY IMPLEMENTING

PID tuning values returned from tuning software must be implemented and tested on the live functioning system to ensure they work as desired. When tuning software is tightly integrated with the control platform, this task is easier because groups of parameters can be applied and changed quickly while observing the system operation (Figure 7). Whenever changes are made to tuning parameters, users must be prepared to restore original tuning values or put the loop in manual.

Tests include adjusting the SP to ensure it is tracked adequately, and also introducing a process load change or disturbance to confirm that the PV recovers quickly. Properly tuned loops respond fast enough to meet the user goals with minimal or acceptable overshoot and controlled CO action. Note that if the loop is first observed at steady state, it is not possible to evaluate the reaction. Since PID controllers work based on error, an error must be introduced to force the controller to respond.

6 Steps to Improved PID Tuning

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STEP 6: DOCUMENT

Documentation is the final step of the loop tuning process and perhaps the most overlooked. The moment a loop is tuned and performing well, the team breathes a sigh of relief and is ready to move on. But the work is not complete without comprehensive documentation including:

Who: Who made the changesWhat: What loop was tuned, what the raw data was, what settings were originally in place, what settings were recommended, and what settings were used as finalWhen: When the loop was tested and adjustedWhy: The reason and goals for the loop tuning

Proper documentation provides a record for future work (Figure 8).

Professional loop tuning software typically offers excellent options for documenting the noted topics, including actual and simulated response trends, ideally in a convenient PDF report format.

Figure 8 – Documenting the process is often overlooked, but is necessary to indicate who made the changes, the loop’s prior and recommended settings, when it was adjusted, and why it was tuned

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Application Example

Williams is an energy infrastructure company operating interstate gas pipeline, gathering and processing operations spanning the United States. The company’s Opal, Echo Springs and Willow Creek gas processing plants have a combined inlet capacity of approximately 2.3 billion cubic feet of natural gas per day, with each monitored around the clock by highly skilled operations personnel. The control platform is a Yokogawa CENTUM VP distributed control system (DCS), with four domains operational across three facilities.

The control systems operate about 1,000 PID control loops in total. Due to the number of PID loops and the need for optimal tuning, Williams has implemented three domain licenses of Control Station csTuner, each integrated natively with the Yokogawa CENTUM VP systems. The software is initiated with a simple right-click menu within the DCS environment.

The Williams team followed the six-step approach described in this paper to successfully tune a de-ethanizer reboiler. During the “find” step, it was determined that operators needed extremely accurate control so they could exactly adjust the de-ethanizer temperature to “slip” a little ethane over to the de-propanizer to maximize profits since the propane stream ethane is worth more than ethane alone.

Because the process was so slow, the doublet test took about 45 minutes (Figure 9). Fortunately, the tuning software was able to overcome the fact that starting conditions were not at steady state. After data gathering, the parameters were adjusted to minimize closed-loop response time and improve SP tracking.

6 Steps to Improved PID Tuning

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Figure 9 – Even for slow processes and non-steady-state starting conditions, tuning software is able to provide optimal tuning parameters

It was noticed that performance of the PID temperature control was impacted by the de-ethanizer operational flow, but in this case the tuning was acceptable at all flow rates (Figure 10). Following the tuning steps allowed the Williams team to achieve improved control, enabling them to safely add heat to the de-ethanizer to make propane with a lower vapor pressure so it could be sold as refrigerant grade.

Figure 10 – For this application, the PID parameters provided by the tuning software for temperature control delivered an acceptable response even at varying flow rates

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Conclusion

PID loop control is a basic element of industrial process automation. While commonly employed, it is also often misused due to poor tuning. Manual and other types of tuning efforts are typically applied, but a solution yielding far better results is provided by dedicated loop tuning software, especially when it is integrated with the user’s control platform. With an understanding of some fundamentals, users can follow a six-step approach using this type of software tool to help them reliably test, tune, implement, and document any control loop.

...a solution yielding far better results is provided by dedicated loop tuning software...

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About the Authors

As the company’s thought leader and chief product architect, Dr. Rice oversees both product development and engineering services. He has published extensively on topics associated with automatic process control, including multi-variable process control and model predictive control. He is an expert in both model-based and advanced controls, including unstable and integrating processes. Dr. Rice has been a featured presenter at numerous industry conferences and partner forums, and he has been the recipient of several industry awards. Most recently Dr. Rice was recognized with the Best Presentation Award during the 2018 Yokogawa Users Conference. Dr. Rice received his Bachelors degree in Chemical Engineering from Virginia Polytechnic and State University, and he received both his Masters and Doctoral degrees in Chemical Engineering from the University of Connecticut.

Bob Rice PhD, Vice President, Engineering,

Control Station Inc.

Shannon Vasseur started in the electrical trade in 1997 working on various construction projects in petrochemical, oil refineries and power generation facilities. He began his career with the Williams Companies in 2009 in the I&E department at the company’s Opal, Wyoming facility. In 2012 he transitioned to Williams’ office in Green River, Wyoming, taking on the role of Controls Technology Specialist where he was responsible for maintaining the Southwest Wyoming area’s DCS, SIS, and PLC hardware and software. Over the years Vasseur has assisted with routine upgrades to the facility’s CENTUM VP DCS while pursuing opportunities to improve plant-wide performance and reliability. His certifications include ISA84 Level I, CCST I and Rockwell Automation Certified ControLogix Programmer.

Shannon Vasseur Controls Technology Specialist,

Williams

The contents of this document are subject to change without prior notice. All rights reserved. Copyright © 2019 Yokogawa Corporation of America.[Ed:01] Printed in USA PROD#:YWP29062018E

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