improving agility, performance, and ... - control global...• nonlinear control – most of the...

By ARC Advisory Group

ARC WHITE PAPER

MAY. 2006

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? ....................... 10

Braskem Improves Quality and Increases Throughput.................. 11

Sterling Chemicals Leverages MPC to Create

Performance-Driven Plant ......................................................... 13

Recommendations ................................................................. 136

THOUGHT LEADERS FOR MANUFACTURING & SUPPLY CHAIN

ARC White Paper • May 2006

2 • Copyright © ARC Advisory Group • ARCweb.com

Companies must manage

performance in real-time by

creating an adaptable

performance-driven organization

that can respond quickly and

profitably to changing market

conditions.

Introduction

In a recent ARC Advisory Group survey widely distributed to companies worldwide, users most often cited Advanced Process Control (APC) tech-nology 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 rea-son why.

Challenges of Modern Manufacturers

The ultimate business objective of any organization is to satisfy its custom-ers’ 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 desir-

able products. Unfortunately, many factors conspire to make this seemingly simple task more difficult. In to-day’s business environment, companies have to contend with intense global competition, reduced technical and operational resources, higher raw material and energy costs, stricter environmental regulations, and capricious 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 as-sets 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 profit-ability 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


Copyright © ARC Advisory Group • ARCweb.com • 3

To increase profitability, companies

are shifting from a commodity focus

to a customer focus that requires

the development of differentiated

products and frequent production

changeovers. Emphasis is moving

from increasing capacity to

optimizing products,

processes, and assets.

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.

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 opera-tions 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 re-quires companies to attain nearly flawless execution. Companies that

approach Six Sigma operations during sustained op-erations and perform grade changes in the shortest amount of time and with the least amount of off-spec product have a distinct competitive advantage.

Clearly, advanced process control has an even greater role to play in a demand-driven environment. APC is a proven technology that reduces process variability and inefficiency, improves product consistency, and allows operations to push constraints to the limits.

With more frequent grade changes, APC is needed to effectively manage

External Forces demand greater agility and asset effectiveness

Increased CompetitionInventory Visibility

Rapid Product Innovation

SRM

Demand Pull

Increased Compliance

Improved Service

Higher Quality

Increased Raw Material &

Energy Costs

Suppliers

Rapid Product Changeover

Fewer People

Customers

Smaller Order Size

Increased Production Visibility

Greater Customization

Greater Product Variety



The use of digital computers for

process control dates back to the

late 50’s and early 60’s, however,

their high cost limited their use to

large scale refinery operations. The

advent of the microprocessor and

the Distributed Control System

(DCS) in the mid 1970’s

revolutionized direct digital control

and supervisory control

methodologies.

transitions and impart the necessary agility in accomplishing customer-centric 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 devel-opment 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. With the rapid adoption of MVC, companies special-izing in developing and implementing advanced control were established. These companies enjoyed much success as they relieved operating companies of the drudgery and high cost of developing and main-taining internally developed and supported software applications.

Today, MVC is successfully used across all industries, 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 blend-ing. 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 perform-ance. 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.



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 cas-cade 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 proc-ess unit more effectively requires the use of more advanced process control tech-niques. 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 algo-rithm or strategy that deviated from the classical three-term, PID control. Today, APC encompasses a variety of control technologies and methods such as super-visory, 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 applica-tions. Typical description of each includes:

• Supervisory Control – analyzes the current situation to determine the best operating policy. In doing so, the supervisory computer coordi-nates the activities of the basic control loops.

Advanced Process Control Value Progression



• 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 of-fer 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 applica-tions 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 interac-tions 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



these issues by taking into account the interactions among the process vari-ables.

MPC is the most pervasive method for multivariable control. MPC uses a model of the process to predict how the process output variables will re-spond to changes in the process input variables and disturbances. This predictive capability allows the controller to determine the best way to ad-just 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 itera-tive 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, first-principle models are often simplified by linearization or some other ap-proximation 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 opti-mization 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 first-principle 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 funda-mentally alter their production methodology. Production runs are becoming shorter with more frequent grade changes to satisfy customer



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 per-formance 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 functional-ity, complicated controller tuning recipes that provide sub-optimal control are often employed. In addition, linear solutions perform poorly for transi-tioning 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 control-lers 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 per-cent 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, multi-variable, and nonlinear affects of a process unit even when the dynamics of the process unit is constantly changing due to changes in operating objec-tive, 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 proac-tively 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.



MPC Enables Continuous Performance Improvement

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 transi-tions 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.

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 ad-justed 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.



How is Pavilion’s MPC Different than Alternatives?

Pavilion Technologies is one of the leading providers of model-based soft-ware 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 solu-tion capability with the release of Pavilion8.

Pavilion8 is a modular software plat-form 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 imple-mented in J2EE and is certified “Powered by SAP NetWeaver.” The platform’s scalability, flexibility and ease of integration with existing busi-ness 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 accu-racy 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 fur-ther 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

Pavilion8 Platform Powers High-Value Industry Applications



a wide range of process conditions. This capability enhances product qual-ity and improves transition management. Grade transitions can be a major source of off-spec product in most manufacturing plants due to the diffi-culty 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 sched-ules 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 com-petitive 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 spe-cific control situations, problems can be isolated and corrective action taken.

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 com-pany produces approximately 5.7 million tons of products per year. The

Pavilion8 Modeling Engine Leverages Multiple Model Types



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 con-trol of polymer reactors. This is not an easy task however, because polymer reactors are complex, multivariable, nonlinear systems that exhibit consid-erable dead-time. Braskem used a variety of traditional linear process control technologies with limited success. To improve control of its reac-

tors, Braskem began a comprehen-sive MPC project in the early 1990’s with the goal to increase production rates and quality, while reducing costs.

The company evaluated several MPC packages from different ven-dors, but chose Pavilion to supply an MPC solution. The first appli-cation was on one of their Spheripol PP line. Braskem’s engineering staff worked with Pavilion to develop and commis-sion the MPC solution.

The initial step was to develop a nonlinear model to predict polymer prop-erties. Since the plant did not have enough data to create pure empirical models that covered the entire operating range of the plant, the online in-ferential 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 control-ler. 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,

Improved Quality with MPC



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 de-viation better than 50 percent on all controlled variables, decreased

transition time, and reduced 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 ap-plications on various polymer lines throughout their organiza-tion. Three of these applications have been online for about 10 years. The key to sustaining the value of these controllers for such a long pe-

riod of time is that Braskem has knowledgeable and dedicated plant per-sonnel 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 sty-rene, acetic acid, and plasticizers. Sterling Chemicals styrene plant in Texas City, Texas produces 1.7 billion pounds of 99 percent or greater purity sty-rene 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.

0

1

2

3

4

5

6

-25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%deviation (SP-PV)/SP

Without APCWith APC

2.4%

CONSTRAINT

Increase in Production Rate Obtained with MPC



Adhering to these principles, Sterling established a partnership with Pavil-ion Technologies as its supplier of MPC and environmental compliance solutions. The relationship began in 1993 when Pavilion delivered its pre-dictive 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 rein-force 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 off-spec styrene product may require reprocessing, which would incur addi-tional 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 produc-tion process. In the case of styrene, managing the reactor catalyst life and performance at a given market condition is criti-cal. However, maintaining the key control variables, like reactor temperature and reactants ratio at the desired or optimum tar-

gets is not an easy task. The board operator has to manipulate multiple, in-teracting 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. There-fore, 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 addi-tion, given the robustness of the model, Pavilion’s MPC solution could

Control to Set-point with MPC



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 essen-tially 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 pre-dictive 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 pay-back well within the company’s one year internal hurdle rate. Sterling went on to deploy MPC on two addi-tional process units at that site. The initial styrene application has been online delivering mil-lions of dollars in value for more than 10 years.

“We have some models in our MPC solution that have been online for more than 10 years

Predictive, Browser -based Insight into Key Controller Metrics Increases Visibility and Controller Uptime



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 Pavil-ion8’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 con-troller’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 in-creases 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 notori-ously 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 manufactur-ing and look at MPC as a key enabling technology.

• Treat MPC as a technology that provides a competitive advantage. De-velop 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.



Analyst: Tom Fiske

Editor: Dick Hill

Acronym Reference: For a complete list of industry acronyms, refer to our web page at www.arcweb.com/Community/terms/terms.htm

APC Advanced Process Control

CAGR Compound Annual Growth Rate

MPC Model Predictive Control

MIMO 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 ex-pressed by ARC in this paper are based on ARC's independent analysis.

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© 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.

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