Improving Agility, Performance, and Profitability with MPC A Decade of Success. Introduction Challenges of Modern Manufacturers...

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1 ARC WHITE PAPER By ARC Advisory Group MAY Improving Agility, Performance, and Profitability with MPC A Decade of Success Introduction... 2 Challenges of Modern Manufacturers... 2 Advent of APC in Manufacturing... 4 Types of APC... 4 What is MPC and how is it Different?... 6 Benefits of MPC over APC... 8 How is Pavilion s MPC Different than Alternatives? Braskem Improves Quality and Increases Throughput Sterling Chemicals Leverages MPC to Create Performance-Driven Plant Recommendations THOUGHT LEADERS FOR MANUFACTURING & SUPPLY CHAIN

2 Introduction In a recent ARC Advisory Group survey widely distributed to companies worldwide, users most often cited Advanced Process Control (APC) technology as providing the greatest benefit among all advanced automation applications. In fact, the benefits are deemed so significant that many manufacturing experts consider the use of APC a necessary requirement to remain competitive. A closer look into the technology may reveal the reason why. Challenges of Modern Manufacturers The ultimate business objective of any organization is to satisfy its customers needs while generating profits and creating value for its shareholders. Successful business performance of a manufacturing company is predicated on how effectively it uses its assets to convert raw material to highly desirable products. Unfortunately, many factors conspire to Companies must manage make this seemingly simple task more difficult. In today s business environment, companies have to contend performance in real-time by creating an adaptable performance-driven organization with intense global competition, reduced technical and that can respond quickly and operational resources, higher raw material and energy profitably to changing market costs, stricter environmental regulations, and capricious conditions. demand. Agility key to asset effectiveness in demand-driven market A common misconception is that increasing capacity utilization is the best way to improve business performance. However, it is not just utilization that determines profitability. It is how effectively companies use their assets to generate value. Even when operating at high capacity, there is a considerable amount of untapped potential in manufacturing assets due to inefficiencies and the inability to pursue higher value opportunities as they arise. Extracting greater value from manufacturing assets (improving Return On Assets - ROA) is a major challenge. Businesses looking to increase profitability are shifting to customer-centric, demand-driven manufacturing where product quality and exemplary customer service is becoming just as essential for success as price. The transition from production-driven to 2 Copyright ARC Advisory Group ARCweb.com

3 demand-driven manufacturing requires a greater variety of higher value products, more frequent changeovers, and shorter production runs. It also requires increased agility and flexibility in manufacturing operations so that plants can quickly respond to planned demand, but also profitably take advantage of new market opportunities when they occur. Suppliers Increased Raw Material & Energy Costs Inventory Visibility Increased Competition Increased Compliance Increased Production Visibility Greater Customization Greater Product Variety SRM Higher Quality Rapid Product Innovation Rapid Product Changeover Improved Service Demand Pull Smaller Order Size Fewer People Customers External Forces demand greater agility and asset effectiveness Moving to a more demand-driven operation offers significant benefits; but it is also more demanding on manufacturing operations. Manufacturers must be prepared to drive out inefficiencies during both steady-state operations and during transitions. Most companies rarely achieve more than three to four Sigmas during steady state operations and typically much lower during transitions. Successful demand-driven manufacturing requires companies to attain nearly flawless execution. Companies that approach Six Sigma operations during sustained operations and perform grade changes in the shortest To increase profitability, companies are shifting from a commodity focus amount of time and with the least amount of off-spec to a customer focus that requires product have a distinct competitive advantage. the development of differentiated products and frequent production Clearly, advanced process control has an even greater changeovers. Emphasis is moving role to play in a demand-driven environment. APC is from increasing capacity to a proven technology that reduces process variability optimizing products, and inefficiency, improves product consistency, and processes, and assets. allows operations to push constraints to the limits. With more frequent grade changes, APC is needed to effectively manage Copyright ARC Advisory Group ARCweb.com 3

4 transitions and impart the necessary agility in accomplishing customercentric objectives and improve overall business performance. Advent of APC in Manufacturing Since the early days of computer control, the refining industry has been a pioneer in developing and using APC. In fact, the genesis of supervisory and multivariable control (MVC) can be traced to the major oil companies. As the refining companies continued to innovate through internal development programs, the industry successfully deployed numerous APC applications on its major process units. The petrochemical industries along with others were quick to adopt MVC technology. The use of digital computers for With the rapid adoption of MVC, companies specializing in developing and implementing advanced process control dates back to the late 50 s and early 60 s, however, control were established. These companies enjoyed their high cost limited their use to much success as they relieved operating companies of large scale refinery operations. The advent of the microprocessor and the drudgery and high cost of developing and maintaining internally developed and supported software the Distributed Control System (DCS) in the mid 1970 s applications. revolutionized direct digital control and supervisory control Today, MVC is successfully used across all industries, methodologies. albeit some more than others. Nearly every refinery has some form of APC on large volume processing units such as crude distillation, fluid catalytic cracking, and gasoline blending. End-users continue to implement advanced process control projects at a rapid pace as evident by the fact that the APC market has averaged more than 13 percent CAGR (compounded annual growth rate) for the last ten years. Types of APC Companies use a variety of different process automation technologies and strategies to control their processes and improve their business performance. Two of the more common approaches are regulatory control and APC. Over the years, these two approaches have evolved and now have different meanings from when they were first conceived. 4 Copyright ARC Advisory Group ARCweb.com

5 The Proportional-Integral-Derivative (PID) feedback control loop has been the workhorse of process automation for more than half a century. The simple single input/single output (SISO) general purpose PID algorithm controls the majority of equipment in the process industries. For instance, a typical manufacturing plant may have hundreds if not thousands of these regulatory loops that perform basic control functions. Since its inception, sophisticated techniques have been developed to improve the performance of the PID controller. Advances such as dead-time compensators and cascade control that were once considered advanced control are now categorized as regulatory control. Although regulatory control provides adequate control in terms of plant safety, it rarely achieves optimal control in terms of quality, nor does it operate in the most economical fashion. Controlling a process unit more effectively requires the use of more advanced process control techniques. As these techniques continue to evolve, the meaning of APC has changed as well. The term Advanced Process Control came into prominent use in the late 1960 s and initially referred to any algorithm or strategy that deviated from the classical three-term, PID control. Today, APC encompasses a variety of control technologies and methods such as supervisory, inferential, feedforward, adaptive, multivariable, nonlinear, and model predictive. Some APC applications incorporate several of these elements while others exclusively focus on one particular aspect. Technologies such as fuzzy logic, expert systems, neural nets, statistics, and rigorous models often form the basis for APC applications. Typical description of each includes: Advanced Process Control Value Progression Supervisory Control analyzes the current situation to determine the best operating policy. In doing so, the supervisory computer coordinates the activities of the basic control loops. Copyright ARC Advisory Group ARCweb.com 5

6 Inferential - uses secondary measurements to adjust the values of the manipulated variables in order to keep the unmeasured controlled variables at a desired level. Feedforward uses direct measurements of the disturbances to adjust the values of the manipulated variables. The objective is to keep the values of the controlled variables at the desired levels. Multivariable Control typically, each process unit requires control over several variables. Systems with more than one control loop are known as multi-input/multi-output (MIMO) or multivariable systems. Model Predictive Control - uses a reference model of the process to predict future process behavior and calculate an optimum set of control moves that minimizes the deviations from the desired control objective. Nonlinear Control most of the control systems today assume that process dynamics are linear or nearly linear because process operations are close to a steady state. However, there are important instances for which the linearity assumption is violated and nonlinear techniques offer better control. Over the last several decades, multivariable Model Predictive Control (MPC) has become the prevailing technology in APC; so much so in fact that the terms are often used interchangeably. Currently, the use of large scale MPC is widespread throughout the process industries. ARC estimates that there are about 10,000 MPC applications with the number of applications climbing rapidly. What is MPC and how is it Different? Controlling a major process unit effectively usually means dealing with multivariable systems. It is extremely unlikely that treating each control loop independently will provide optimal control. In most situations, the control action of one loop affects the other loops. When significant interactions among the loops exist, optimizing loops independently usually results in unstable situations. It is possible to detune the loops to increase stability, but at the price of diminishing efficiency. Multivariable control addresses 6 Copyright ARC Advisory Group ARCweb.com

7 these issues by taking into account the interactions among the process variables. MPC is the most pervasive method for multivariable control. MPC uses a model of the process to predict how the process output variables will respond to changes in the process input variables and disturbances. This predictive capability allows the controller to determine the best way to adjust the process input variables to drive process output variables to their optimum targets while considering interactions and remaining within any imposed constraint specifications. The key that differentiates MPC from other APC technologies is the use of a model. Even within MPC technology there is a large variety of models used that can make a difference in accuracy and applicability. The models for MPC can be fundamental, empirical, or a combination of both. Pure fundamental models require hundreds of equations to be solved in an iterative manner at each execution. This computationally intensive control problem limits the size of the application to a few input/output variables or where the process dynamics are very slow. To speed up execution, firstprinciple models are often simplified by linearization or some other approximation to reduce the number or equations necessary to solve, but at the expense of introducing model errors. Purely empirical models use only historical process data and require no knowledge of the process or the model structure. One of the more common empirical modeling techniques used in the process industries is the neural network. A neural network model is computationally efficient, allowing it to execute fast in both directions for prediction as well as control and optimization applications. While these models are accurate over the operating range represented by the historical data, they may not perform as well when extrapolated beyond this range. Hybrid techniques allow process knowledge in the form of a mathematical equation or known constraints to be used in the development of the model. The combination of empirical modeling techniques such as neural networks, process data, and firstprinciple equations results in a more robust, more accurate model across the entire range of operating parameters. Enhanced Transition Capability with Nonlinear MPC The shift to demand oriented manufacturing requires companies to fundamentally alter their production methodology. Production runs are becoming shorter with more frequent grade changes to satisfy customer Copyright ARC Advisory Group ARCweb.com 7

8 requirements. Operating these assets safely and at peak efficiency, even during state changes, is vital to remain competitive. APC solutions must provide the manufacturing foundation for exceptional operational performance through tight process control with a high degree of manufacturing agility and responsiveness. There are several problems associated with applying traditional APC and linear MPC solutions to nonlinear processes. Linear solutions perform well only on a narrow range of operating conditions. To extend the functionality, complicated controller tuning recipes that provide sub-optimal control are often employed. In addition, linear solutions perform poorly for transitioning from one operating region to the next. Nonlinear control overcomes many of the shortcomings of linear control by incorporating a single set of tuning values valid over the entire operating range and can calculate an optimal transition trajectory. Nonlinear controllers are popular choices in the polymer industries, but are finding numerous applications elsewhere as well. Benefits of MPC over APC Over the years, MPC has decisively demonstrated its value. Many leading companies have successfully applied MPC to their most important process units. Significant benefits include increasing throughput by up to five percent and improving yields up to ten percent. Achieving those benefits means that the MPC solution must be able to maintain control of a process unit at the most favorable economic operating point often at the intersection of constraints without violating them. A MPC solution must have an accurate model to handle the complex, multivariable, and nonlinear affects of a process unit even when the dynamics of the process unit is constantly changing due to changes in operating objective, equipment degradation, and catalyst deactivation. With an accurate model of the process, MPC provides tight control through transitions allowing operations closer to constraints, and responding proactively and adaptively to disturbances. Without accurate models, the control cannot act as aggressively and safety cushions must be implemented around constraints. Thus, a good model helps avoid this conservatism. 8 Copyright ARC Advisory Group ARCweb.com

9 The economic benefits of MPC come from running at maximum throughput constraints or minimum energy and raw material consumption constraints, while maintaining product quality within specification. MPC makes this possible by accurately knowing where the process is now and accurately predicting where it is going in the future. Although demand-driven manufacturing requires more frequent transitions and shorter production runs, it also offers greater business rewards by creating the ability to capture new market opportunities. MPC provides the tight control and agility to confidently and profitably make the necessary transitions without compromising product quality and plant safety. MPC Enables Continuous Performance Improvement Typical payback of MPC projects are between three to eighteen months. However, the real challenge is sustaining MPC the long-term benefits of MPC. The performance of an MPC application can deteriorate over time due to changes in the process or equipment. Depending on the change (i.e. catalyst type, historian changeover, etc.), controllers may need to be adjusted to maintain maximum benefits. Without proper corporate support for the value of controller uptime, the MPC application may fall into disuse. Maintaining the benefits requires that companies recognize the value and have a sound plan to ensure that the right solutions, people, and processes are in place to sustain the value. Copyright ARC Advisory Group ARCweb.com 9

10 How is Pavilion s MPC Different than Alternatives? Pavilion Technologies is one of the leading providers of model-based software used by leading manufacturers to enhance their profitability by improving their production processes. Pavilion has built its reputation by implementing its non-linear, empirical modeling technology and its robust model-predictive control solutions to solve some of the most difficult and challenging problems in manufacturing. Pavilion recently extended it solution capability with the release of Pavilion8. Pavilion8 Platform Powers High-Value Industry Applications Pavilion8 is a modular software platform and the foundation for Pavilion s industry-specific solutions. Leveraging a hybrid modeling engine at its core, Pavilion8 includes modules to control, analyze, monitor, visualize, warehouse, and integrate that are combined into high-value applications. Based on a modern Service-Oriented Architecture (SOA), the Pavilion8 platform is implemented in J2EE and is certified Powered by SAP NetWeaver. The platform s scalability, flexibility and ease of integration with existing business and plant infrastructure help to lower the total cost of ownership. The hybrid modeling functionality within Pavilion8 allows the user to take advantage of all of the known information (empirical data, first principles equations, equipment specifications, etc.) to build models with higher accuracy than purely empirical models and to address larger control applications than is possible with purely fundamental models. This allows manufacturers to expand their product portfolio by stretching their normal process operating range without expensive step testing. Once operational data is collected for the new product, the existing hybrid model can be further refined for even better control. Pavilion8 s nonlinear model predictive controller with variable dynamics explicitly accounts for the changing dynamic behavior of the process across 10 Copyright ARC Advisory Group ARCweb.com

11 a wide range of process conditions. This capability enhances product quality and improves transition management. Grade transitions can be a major source of off-spec product in most manufacturing plants due to the difficulty in controlling the process smoothly through the entire transition. In order to minimize this transition product, many manufacturers use a product wheel to make very small transitions in a particular sequence, which severely limits production scheduling flexibility. Pavilion8 accounts for the varying dynamic behavior of the process across grades. Even transitions from one end of the product spectrum to the other, can be accomplished efficiently with minimal off-spec production. This also provides flexibility to adjust production schedules to meet new orders, regardless of what is currently being produced. Sustaining value over the lifecycle of the advanced control application is essential to maintaining a competitive advantage. Evaluating control performance issues with complex multivariable controls can be challenging. Pavilion8 provides effective controller performance metrics to help pinpoint problems by quantifying utilization, variability, deviation from target, and time at constraints for manipulated and controlled variables. By being able to correlate quality problems with specific control situations, problems can be isolated and corrective action taken. Pavilion8 Modeling Engine Leverages Multiple Model Types In many ways Pavilion8 supports a company s need to extract greater value from their manufacturing assets by reducing variability, improving product quality, and enabling improved transition management that is required for demand-driven manufacturing. In addition, Pavilion8 helps to sustain the value of MPC, which is critical to maintaining a competitive advantage. Braskem Improves Quality and Increases Throughput Braskem is the largest petrochemical company in Latin America. The company produces approximately 5.7 million tons of products per year. The Copyright ARC Advisory Group ARCweb.com 11

12 company operates numerous polyethylene and polypropylene production lines throughout its 13 manufacturing facilities. The company is dedicated to continuously improving its competitive position by developing new products and adopting technologies to enhance its production capability. There are significant financial and competitive incentives to improve control of polymer reactors. This is not an easy task however, because polymer reactors are complex, multivariable, nonlinear systems that exhibit considerable dead-time. Braskem used a variety of traditional linear process control technologies with limited success. To improve control of its reactors, Braskem began a comprehensive MPC project in the early 1990 s with the goal to increase production rates and quality, while reducing costs. Improved Quality with MPC The company evaluated several MPC packages from different vendors, but chose Pavilion to supply an MPC solution. The first application was on one of their Spheripol PP line. Braskem s engineering staff worked with Pavilion to develop and commission the MPC solution. The initial step was to develop a nonlinear model to predict polymer properties. Since the plant did not have enough data to create pure empirical models that covered the entire operating range of the plant, the online inferential Soft Sensor models incorporated physical system equations along with knowledge of the workers and neural network models. In 1997, nonlinear controllers were installed to control the polymer properties and other key reactor variables. Typically Braskem introduces one or two new products into production each month. These process modifications are well handled by the controller. In addition, the company confidently makes one to two transitions per day with very little off-spec product. We were the first polymer manufacturer to adopt Pavilion s nonlinear MPC technology, said Esdras Demoro, Automation Corporate Manager, 12 Copyright ARC Advisory Group ARCweb.com

13 Braskem. This gave us a competitive edge that still serves us after more than 10 years. Braskem is reaping significant benefit from Pavilion s MPC solution. The company has increased production by 2.4 percent, improved standard deviation better than 50 percent on all controlled variables, decreased transition time, and reduced 6 Without APC With APC 5 4 CONSTRAINT 3 2.4% % -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% deviation (SP-PV)/SP Increase in Production Rate Obtained with MPC costs. The estimated value of Braskem s benefits is around $2 million per year. The company has deemed the project so successful that they implemented several more applications on various polymer lines throughout their organization. Three of these applications have been online for about 10 years. The key to sustaining the value of these controllers for such a long period of time is that Braskem has knowledgeable and dedicated plant personnel involved in developing the applications and making sure the models are kept up to date. In addition, Pavilion has provided tools that supply detailed performance metrics on the process and the controller. Sterling Chemicals Leverages MPC to Create a Performance-Driven Plant Sterling Chemicals is an intermediate commodity chemical supplier of styrene, acetic acid, and plasticizers. Sterling Chemicals styrene plant in Texas City, Texas produces 1.7 billion pounds of 99 percent or greater purity styrene monomer annually. Sterling's strategy is to be a quality leader and in the top quartile as a low cost leader in supplying its intermediate chemicals. The company s guiding principles include leveraging technology to lower costs, doing things right the first time, satisfying customers needs, and building strong alliances with process technology leaders. Copyright ARC Advisory Group ARCweb.com 13

14 Adhering to these principles, Sterling established a partnership with Pavilion Technologies as its supplier of MPC and environmental compliance solutions. The relationship began in 1993 when Pavilion delivered its predictive emissions monitoring solution (PEMs). Since then, the two companies have implemented several MPC applications on three of Sterling s production lines. Sterling Chemicals pursued model predictive control technology to reinforce their strategy of consistently delivering products with purity of 99% or greater. To ensure customer satisfaction, Sterling needed to meet narrow purity specifications. However, both off-spec product and product quality give-away (over purified) product can be very costly. For example, an offspec styrene product may require reprocessing, which would incur additional energy consumption and some product loss. In addition to quality, Sterling Chemicals is a low cost producer. To support this goal Sterling wanted their MPC investment to reduce raw materials cost through yield improvements. Traditional regulatory control and linear MPC solutions could not meet the challenge of the highly nonlinear styrene production process. In the case of styrene, managing the reactor catalyst life and performance at a given market condition is critical. However, maintaining the key control variables, like reactor Control to Set-point with MPC temperature and reactants ratio at the desired or optimum targets is not an easy task. The board operator has to manipulate multiple, interacting variables to hit those targets. This results in wide variability of the key control variables from shift-to-shift or even from day-to-night. Therefore, reducing the variability of the key control variables consistently is the key to improve raw materials usages. Pavilion s MPC solution offered a robust non-linear model and the ability to control with varying dynamics. As a result, they are able to handle very difficult distillation and reactor operations in the styrene process. In addition, given the robustness of the model, Pavilion s MPC solution could 14 Copyright ARC Advisory Group ARCweb.com

15 cover a wide range of production operations, even in areas where Sterling lacked test data. Sterling s MPC application has changed the way the team operates the plant in the following ways: Reduced board operator errors. The MPC solution significantly improved consistent operation from operator to operator, and shift to shift. Board operator is focused on higher valued tasks. The MPC essentially eliminated the battle of constantly fighting the process, and has actually shifted their focus to the bigger picture of the unit operation, such as the overall health of the regulatory system and pushing and optimizing the control targets. Better instrument and analyzer drift detection. Because of the predictive nature of the MPC, it is able to detect drift and outlier of online analyzers or PID loops before they compromise expected production results. Sterling first deployed MPC on the styrene production process to: Reduce energy usage by more than 5 percent Increase production capability by more than 2 percent Increase product quality by reducing impurity variability 75 89% The solution delivered a payback well within the company s one year internal hurdle rate. Sterling went on to deploy MPC on two additional process units at that site. The initial styrene application has been online delivering millions of dollars in value for more than 10 years. Predictive, Browser -based Insight into Key Controller Metrics Increases Visibility and Controller Uptime We have some models in our MPC solution that have been online for more than 10 years Copyright ARC Advisory Group ARCweb.com 15

16 with little maintenance, said Dennis Yieh, Control Technologist, Sterling Chemicals. Pavilion s MPC technology is very robust, which allows us to operate within a wide range of production rates to meet customer demands or price pressures. As a result, the total cost of ownership is small, relative to the millions of dollars in value we are realizing from the solution. Sterling Chemicals has upgraded their control solution to the new Pavilion8 platform. In addition to the variable dynamic control features, Sterling is taking advantage of the controller performance metrics available in Pavilion8 s visualization module. This gives the entire team operators, process engineers, and operations management real-time, browser-based access to controller performance. The ability to scroll back in time and see the controller s performance in certain conditions is a key feature that facilitates trouble-shooting. Pavilion8 helps Sterling to maximize controller uptime and increase the overall value to the company. Recommendations MPC is a proven technology that improves plant and asset performance by reducing process variability and increasing asset utilization. It also increases production agility to facilitate demand-driven manufacturing to improve ROA. MPC inherently models the process dynamics to provide tight control over the complete operating range of the plant. In addition, processes where disturbances cause control difficulties are ideal for MPC since measured disturbances are automatically compensated for with the model. Consequently, disturbances will have minimal detrimental impact on the process. Processes that have strong interacting variables are notoriously difficult to control effectively with conventional techniques. Multivariable control, on the other hand, can simultaneously drive all controlled variables toward targets or focus on a single variable without causing the other variables to deviate from set-point. Companies should consider the benefits of demand-driven manufacturing and look at MPC as a key enabling technology. Treat MPC as a technology that provides a competitive advantage. Develop and maintain best practices and shared learning to obtain the most value from your MPC investments. Establish strong relations with your advanced technology provider to help you sustain the value of your investment. 16 Copyright ARC Advisory Group ARCweb.com

17 Analyst: Tom Fiske Editor: Dick Hill Acronym Reference: For a complete list of industry acronyms, refer to our web page at APC CAGR MPC MIMO Advanced Process Control Compound Annual Growth Rate Model Predictive Control Multiple Input/Multiple Output MVC Multivariable Control PID Proportional-Integral Derivative ROA Return on Assets SISO Single Input/Single Output Founded in 1986, ARC Advisory Group has grown to become the Thought Leader in Manufacturing and Supply Chain solutions. For even your most complex business issues, our analysts have the expert industry knowledge and firsthand experience to help you find the best answer. We focus on simple, yet critical goals: improving your return on assets, operational performance, total cost of ownership, project time-to-benefit, and shareholder value. All information in this report is proprietary to and copyrighted by ARC. No part of it may be reproduced without prior permission from ARC. This research has been sponsored in part by Pavilion Technologies. However, the opinions expressed by ARC in this paper are based on ARC's independent analysis. You can take advantage of ARC's extensive ongoing research plus experience of our staff members through our Advisory Services. ARC s Advisory Services are specifically designed for executives responsible for developing strategies and directions for their organizations. For subscription information, please call, fax, or write to: ARC Advisory Group, Three Allied Drive, Dedham, MA USA Tel: , Fax: , info@arcweb.com Visit our web page at ARCweb.com 2006 Pavilion Technologies, Inc. All rights reserved. Pavilion and Soft Sensor are registered in the U.S. Patent and Trademark Office. Pavilion Technologies and Pavilion8 are U.S. trademarks. Predictable Results. Guaranteed. is a U.S. service mark of Pavilion Technologies, Inc. Copyright ARC Advisory Group ARCweb.com 17

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