Making APC Perform 2006 ExperTune, Inc. George Buckbee, P.E. ExperTune, Inc.

Making APC Perform 2006 ExperTune, Inc. George Buckbee, P.E. ExperTune, Inc.

Summary Advanced Process Control (APC) promises to deliver optimal plant performance by optimizing setpoints, decoupling interactions, and improving control response. To have a successful APC application, you must continuously monitor and assess the performance of the advanced controller. In addition, you should establish and maintain a solid foundation for control. Sensors, valves, and controllers must be continuously monitored to ensure optimal operation. A performance supervision system should be applied to make sure that each part of the APC package can perform its tasks properly. Introduction This paper provides guidance for establishing and maintaining an Advanced process Control (APC) package in a process manufacturing environment. In its most common form, the APC control is a form of Model Predictive Control, or MPC. This paper will focus on MPC applications, although the concepts apply to other forms of APC as well. The MPC controller uses a dynamic model of the process to determine control actions. A multi-input, multi-output model allows for decoupling of control interactions, as well as coordination of control movements to drive the process toward an optimum condition. Controlled Variables MPC Controller Manipulated Variables Disturbance Variables The MPC Controller is a complex piece of software, usually running on a dedicated computer. It is critical that the MPC controller itself is running well. That means that we must ensure: The MPC Model is Accurate The MPC Controller is operating in its best state Constraints are being effectively managed MPC objective functions are being met MPC output moves are not excessive

The theory is great. The math is beautiful. But the process exists in the real world. It is typical for MPC applications to be running in full automatic mode far less than 80% of the time. Real-world issues, such as sensor failures, control valve stiction, and changes in process operation can cause the MPC system to perform poorly, or even to fail. In any of these circumstances, the most common course of action is to take the MPC system off-line. To keep the MPC controller function properly, we must make sure that the real world is performing at its best. We can do this through the application of a performance supervision system. Such a system will monitor, track, prioritize, and report on: Performance of the MPC Controller Performance of the Process Itself Performance of independent controllers Performance of Sensors Performance of Control Valves Through application of a Performance Supervision System, you can ensure that: 1. Conditions are right for the introduction of MPC. 2. All parts of the process and control system remain in a good range of performance. 3. The MPC controller itself is performing properly. The paper is organized as follows: 1. Review of important factors for MPC control 2. Key Specifications for applying a Performance Supervision System to Advanced Process Control

Review of Important Factors for MPC Control Performance Overview To ensure performance of the MPC Control system, we must first establish a solid foundation. Otherwise, the MPC system becomes just a piece of software, running by itself on a computer. The solid foundation includes making sure that the following conditions are established and maintained in the plant: Accurate, Repeatable Process Models MPC Controller Performs as Designed Process and Operational Readiness Sensors and Valves in Good Condition Independent Controls are Robust Ability to Track Plant Performance Each of these factors is explored in more detail below. Accurate, Repeatable Process Models Of course, MPC control requires a good dynamic model of the process. By good, we mean that the model must accurately reflect the process, its interactions, and the process dynamics. Since the MPC controller itself is related to the model inverse, any imperfections in the model will cause performance issues with the MPC controller. The starting point for a good multi-variable process model is cross-correlation analysis. Using cross-correlation to understand interactions within the plant, you can properly define the scope and boundaries of the MPC controller. If your performance supervision system is integrated with modeling and loop optimization software, then you can capture process model data, which can form the basis of the MPC s multi-variable model. All models are imperfect. There will always be some modeling error. That is, the MPC s model of the process will always differ from the actual process response. Maintaining good control with MPC requires that the model continues to be accurate enough to provide good control.

Other factors affect the accuracy of the model: If the controller runs into constraints, such as fully opened control valves, then the model order changes. When production rates change, process deadtimes and time constants also change. Process upsets may force the process into new operating ranges Heat exchangers fouling Control valves degrading (hysteresis, stiction, other problems) Operation in a non-linear region. To make sure that this is the case, you should make sure that you can track the modeling error on every measured variable (MV), as well as aggregate model error. MPC Controller Must Perform as Designed How will you know if the MPC controller is performing as designed? You must be able to track the following items: What percentage of time is the MPC controller active? How well is it meeting its objective function? What is the overall health of the MPC controller? How often is it running into constraints? When you track these items, the MPC controller will operate closer to its optimal state. If the MPC controller fails to do its job for any reason, some of these measures could be used as a trigger for action. Process and Operational Readiness A stable operating environment is one of the very first pre-requisites for advanced control. If the operator is inundated with alarms, chasing upsets all day, you can be sure that he will not want to respond to another set of alarms. The MPC system will quickly be switched off, and it will never get a chance to perform its functions. Furthermore, since most MPC models are only accurate over a range of operation, you must make sure that the process is stable enough to remain in that range of operation. If operations are routinely bypassed, controllers left in manual, and process operating strategy changes frequently, the MPC model will be inadequate. To ensure that MPC will be effective, you should first address the issues of operational stability, operator changes, and alarm management.

Sensors and Valves in Good Condition How many APC projects have failed because of bad sensors and valves? Too many to count. In fact, one of our clients once installed a $200,000 APC package to eliminate interactions and cycling in their plant, with absolutely no results. Why not? Because the source of the cycle was a sticking valve! If they had focused some time and effort on valves, they could have solved the cycling, and avoided spending the $200,000. (Or at least postponed the spending until they were truly ready.) Sensors are, of course, critically important to the performance of ANY control system. If you have bad readings, then you have bad control. Monitoring the condition of sensors, communications, signal noise limits, and variability are required to ensure the sensor itself is being maintained in good condition. Independent Controls are Robust The MPC Controller is not an island. It is not likely that you will apply an MPC controller to the entire plant. Typically, the MPC controller is specified for a certain unit operation, or closely-coupled parts of the process. Many independent PID controllers will exist around the periphery of the MPC system. These independent PID controllers may have a direct impact on parts of the MPC process. They may impact upstream feeds to the MPC process; they may draw utilities from common headers, or use other common resources. These controllers must be properly tuned for robust performance. They should eliminate upsets in their area of responsibility, without creating undue oscillations downstream. Ability to Track Plant Performance The ultimate objective of MPC is to optimize the plant operation for profitability, quality, and production rates. Correlating these results to the performance of the MPC controller will help to quantify the value of the MPC controller. It will also help you to understand when to spend time, money, and effort updating the MPC models. Some of the key factors that should be tracked at the Unit operations level include: Throughput - Production Rate Quality Reliability / Uptime Energy Costs Material Costs With performance supervision, you can correlate these to controller performance.

Key Specifications for Applying a Performance Supervision System to Advanced Process Control Overview of Performance Supervision A Performance Supervision System uses the DCS or historian as a window into the process, sorts through the mountains of data, and delivers targeted, actionable knowledge about the process. Because it is looking at the entire plant, the Performance Supervision System can find all manner of process, equipment, and control problems. Performance Supervision has been used to uncover failing pump seals, problematic valves, fouling heat exchangers, improper repairs, sources of process upset, controller tuning problems, and changes in process behavior. In short, a Performance Supervision System looks after all the pre-requisites and on-going monitoring needs for Advanced Process Control. Watch over MPC Controller Functionality The performance supervision system will also watch over the MPC controller itself. There are at least 20 performance assessments that apply to an MPC controller. This includes: Assessment of Modelling error Assessment of Objective function Model state changes Controller Entropy % Of Time at Limits % of Time at Rate-of-change limits These assessments provide some specific diagnostics of the performance of the MPC controller. When you establish action limits, or thresholds for each of these assessments, the performance supervision system can take over, even providing you with alert notifications by e-mail or even pager or cell phone. The section below details some of the most important assessments of MPC controller performance: MPC On Time Advanced controls are only adding value when they are being used. If the operator disables the advanced controls, they are not able to add value. A high-level measure of MPC performance is the on time. This is simply a measure of the percent of time that the controller is on-line.

Model Error In most processes, you can expect the actual process to change over time, due to changes in operating strategy, increased production rates, changes in product, changes in raw materials, or many other causes. The performance of an MPC controller depends on the accuracy of the underlying process model. When the model no longer accurately represents the process, control performance will suffer. Updating the model can be an expensive, time-consuming proposition. So you only want to update the model when you are sure that an update is required. A performance supervision system will monitor the modeling error, track the error over time, and establish action limits for it. Based on these action limits, you can make an intelligent decision about whether the MPC model requires updating. Furthermore, the performance supervision system is already gathering information that can be used to expedite the modeling process. Controller Entropy An MPC controller may adjust a variety of manipulated variables to correct deviations in a single controlled variable. Most control output moves come at some cost to process operation or stability. The control entropy, in a way, measures the efficiency of the MPC controller to make the smallest possible control moves to satisfy the control objectives. % Time at Limits When control valves are fully open or fully closed, they are not able to help in the process control. Similarly, controlled variables that reach their limit are no longer providing information about the process. Using this assessment, you will understand the limits of process capability, and its impact on the MPC controller. % Time at Rate-of-Change Limit Many MPC controllers support rate-of-change limiting. This is a constraint on control actions. The more constraints you hit, the less effective the control. By tracking and managing the constraints with a performance supervision system, you will get more performance from your MPC controller.

Model State Changes As the MPC controller reaches a constraint, it loses degrees of freedom, and must adjust its control strategy appropriately. These changes in control strategy may result in changes in control behavior. When we assess the rate of model state changes, we are getting another view of the effectiveness of the controller. A high rate of model state changes means that the controller is frequently changing strategies, resulting in inconsistent operation. MPC Health Every business is different. Every process is different. To optimize your process for your business, you may wish to choose a specific combination of performance measures. MPC Health is a patented technology allows you to develop your own customized, proprietary measures of performance. Automatically Capture Process Models Developing MPC models takes a lot of time. You must gather dynamic response data by making a series of bumps to the controller outputs (valves) in question, then monitor and plot process variable responses, then calculate the individual models. But there is a better way Active Model Capture Technology works by monitoring the process continuously, 24 hours a day. It looks at process changes created by setpoint changes, mode changes, and disturbances to develop a dynamic model of process behavior. The model is then validated and recorded. Model parameters are then compared against historic baseline conditions. If a significant change has occurred, or if the model has drifted from normal range, then a problem is identified. Supervising Process and Operational Performance A Performance Supervision System will focus your attention on the parts of the process that are causing the greatest economic impact. Process upsets can come from a wide variety of sources, such as: Operational Issues Equipment issues Control Issues

Using assessments of each of these factors, and combining it with economic relevance, we can very quickly focus on those things that will result in improved process performance. A few key items to focus on for improvement of operational performance include: Number of alarms for each loop % of Time in Normal Mode Number of operator interventions (mode changes, SP changes, etc.) The performance supervision system will assess these numbers on every loop, and report them to you in priority order. It is quite typical, for example, for 80% of alarms to come from less than 5% of the process variables in the plant. Focusing on these few areas will get the plant ready for APC in the least amount of time. Focus Attention on Equipment Problems The performance supervision system performs assessments on many equipment issues. For example, it is possible to calculate the amount of process upset that is caused by valve stiction on each and every process variable in the plant. Automated prioritization can direct your maintenance team to focus their energies on solving the right problems. Get to the root of the problem quickly by using tools for assessing valve hysteresis and stiction. Since most of the analysis is automated, this testing takes very little time, and does not require a high level of expertise. Most process equipment presents warning signs before it starts to fail. The performance supervision system can watch for the tell-tale signs of impending equipment failure, and provide enough warning to prevent catastrophe. We have seen many, many examples up impending pump failures, which have been diagnosed by looking at related variables, such as variability of flow measurements. According to one user: The operators would never have seen the problem without performance supervision software. Keep Independent Controls at their Optimum Each PID loop must be managed for good performance. However, the definition of good performance may change from loop to loop, from process to process. Some combination of the following criteria for PID Loop Performance may apply:

No overshoot Fast response to setpoint changes Slow, over-damped response Quick disturbance rejection ¼-amplitude damping Minimize interaction with other loops Meeting these requirements means that you must have a flexible approach to controller tuning and optimization. The tuning package will be able to help this by providing: Flexible criteria for speed of response Ability to measure relative response time for loop coordination A method to interpret and adjust controller robustness. Simulation of tuning results in response to setpoint changes, load changes, and noise. Design of compensators for non-linear measurements and non-linear control response. Identify and Eliminate Sources of Oscillation Oscillation is a symptom of sub-optimal performance. When the process is cycling, it will not be performing at its best. To apply advanced controls to this situation is a waste of time. To have the best chances for success of advanced control, you must first identify and eliminate sources of oscillation. Sources of oscillation may include: Routine Process Upsets Controller Tuning Equipment Problems A performance supervision system will continuously analyze process data to: Identify Oscillations Identify the Source of the Oscillations Quantify the period and magnitude of the oscillation Allow analysis & comparison of oscillations affecting each part of the plant. Using these tools, you can very quickly hunt down and eliminate the source of variation. This helps to lay the foundation for advanced controls. With a stable foundation in place, you are better prepared for implementation of advanced controls.

Record, Trend, and Report on Overall Plant Performance A good performance supervision system will also track high-level unit performance measures. These can then be displayed in the same reports with MPC performance measures. Drawing correlations is very direct. This is one of the best ways to quantify the bottom-line value of your control improvement activities. Performance Supervision Systems also make it easy for each user to look at the information in ways that are meaningful to their job function. For example, the maintenance manager may want to compare valve performance measures to his ongoing maintenance costs. Process Engineers may wish to see the shift in process models over time, or the Opportunity Gap that could be closed by shifting setpoints closer to the optimum. These assessments are directly available in trend and report format, and can be easily customized by each user to meet their specific needs. Conclusions A Performance Supervision System will establish and maintain optimal performance for an MPC advanced process controller. Every part of the advanced control strategy is made to work in concert, from the MPC controller itself, down to the sensors, valves, PID controllers, and operating process. Ignore any part of this chain, and you will invite a loss in performance. Performance Supervision watches over the entire advanced control strategy, pushing the plant to its best operational performance. PlantTriage is a registered Trademark of ExperTune, Inc. ExperTune is a registered Trademark of ExperTune, Inc. 2006 ExperTune, Inc. For more information about PlantTriage, or to discuss any of the content of this white paper, please contact George Buckbee, Director of Product Development at ExperTune, Inc. at (262)369-7711 or email sales@expertune.com. About PlantTriage PlantTriage is a Plant-Wide Performance Supervision System that optimizes your entire process control system, including advanced controls, instrumentation, controllers, and control valves. PlantTriage also includes tools to measure and optimize process performance. Find out more about PlantTriage at http://www.expertune.com/planttriage.html

About the Author George Buckbee, P.E. is Director of Product Development at ExperTune, Inc. George holds a B.S. and M.S. in Chemical Engineering, and has over 20 years of direct applications experience in the Process Control field. George is responsible for development of ExperTune s PlantTriage Performance Supervision System and PID Tuner/Analyzer. George can be reached at (262)369-7711 or email george.buckbee@expertune.com.