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PAT: The Value ofBetter Control

W H I T E P A P E R

Predictive control is

about knowing what

is ahead, not what

you just hit.

By Michael Tay and Dennis WilliamsPavilion Technologies

Figure 1

Value of PAT Projects

The Process Analytical Technology (PAT) initiative has as its basis the objective to understandspecific pharmaceutical processes more thoroughly. An improved understanding is assumedto offer higher levels of customer assurance, therefore reduced compliance oversight require-ments as well as a well understood opportunity for higher manufacturing efficiency in termsof reduced off-specification production, reduced give-away and higher capacity utilization.This improved understanding is to be achieved using a variety of analysis tools, including notonly new sensor technology but also process modeling with statistical, fundamental or artificialneural network modeling. PAT-based projects are attempting to apply new sensor, analysis,and control software and technology to improve process knowledge and achieve an intelli-gently controlled process.

In multiple forums, the FDA has reinforced the objective of PAT is to improve process under-standing, whereas one result is the potential to reduce regulatory oversight and burden oncompanies who have demonstrated good practices and a better understanding of theirprocess through proper PAT implementation.

Once a process is better understood, the next step is to directly use that understanding as anenhancement to basic level control and directly control quality and the end-product objective.When you control directly to analyzed, measured or understood quality, you are enforcing agood, in-control process -instead of controlling and documenting that the process ismaintained within static operating limits. (see Figure 1)

There are two classical technologies to regulate process operations: regulatory control (PID) andmodel-predictive control. Better analytics enable the usage of more robust control technologies.

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Regulatory Control

As analytic technologies are applied to our

processes, we can leverage the better under-

standing that results to achieve better control.

Regulatory control, generally PID, is the

workhorse of the control technologies used

in some form on almost every control loop.

In general, control is managed to a target

for temperature, level, pressure or flow, and

an actuator is adjusted to maintain the

process condition at target. This is the

simplest model-based controller that includes

tuning constants to react to proportional

changes in target error, integrated error

that avoids long-term offset, and derivative

action that responds to the speed of the

control response. Given a dynamic process

model and a controller objective, IMC

W H I T E P A P E R

Figure 2

See the Fonterra Case Study atright for a glimpse at example

benefits from dryer control.

MPC Case Study – Drying

Company: Fonterra Co-operative Group Ltd. is a leading multinational dairycompany with an asset base of approximately U.S. $6 billion.Fonterra’s global supply chain stretches from the farms of 13,000shareholders in New Zealand to customers and consumers in 140countries. Collecting more than 13 billion liters of milk a year, Fonterramanufactures and markets over 1.8 million tons of product annually,making it the world's leader in large scale milk procurement,processing, and management.

Fonterra's global ingredients division, NZMP, is the largest dairyingre-dients organization in the world, manufacturing ingredients in fourmain product groups: milk proteins, milk powders, cheese ingredients,and cream products.

Customer Goals: As a business that exports 95% of its production, NZMP placesstrategic importance on continually improving performance andprofitability across its entire manufacturing and supply chain. For thecompany's production facilities, this means a focus on accommodatinggrowing volumes of milk and efficiently converting them into value-added products that are closely aligned with customer needs. NZMPbegan evaluating alternatives to increase throughput to handlegrowing raw milk supplies, seeking ways to improve the operatingefficiency of existing evaporators and dryers before making newcapital investments.

Control Solution: Fonterra/NZMP turned to Pavilion Technologies in 1995 to help improveoperating efficiency and optimize evaporation, drying, filtration, andenergy production processes and since then has deployed Pavilion's APCsolutions at seven sites to improve production in 88 process clusters.

The company's Kauri K2 milk powder plant in New Zealand'sNorthland chose to deploy Pavilion's APC solutions on two evaporatorsand one spray dryer, successfully increasing the yield and quality ofnutritional, whole milk, and skim milk powders. Pavilion's APC solutionallowed Fonterra to exceed its corporate objective of 50% variabilityreduction on both the evaporator and dryer processes. The evaporatorsolution successfully reduced total solids variability by approximately73% and 68% for the two evaporators. The dryer solution reduced thevariation in the sifter moisture by 52%. As a result of Pavilion'ssolutions, the Kauri K2 plant reduced costs, increased throughput,improved product quality, and realized positive financialbenefitsimmediately after installation.

"Pavilion software already installed has increased our plant throughputs, yields, and

profitability through tighter specification control and reduced operating costs. APC project

audits demonstrated attractive returns on investment within months of installation."

Max Parkin

NZMP, Director of Manufacturing and Milk Supply, Fonterra

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(internal model control) tuning rules can

be used to calculate an optimal PID tuning.

These rules are the basis of most regulatory

controller tuning packages.

The results of analytic tools enable a pairing

of each control target (pressure, temperature

or flow) with the best actuator. With an

understanding of the relative influence of

each actuator on each target objective, called

relative gain analysis (RGA), in control terms

the mathematical model can be used to

design the optimal regulatory control

strategy. In many cases the regulatory control

loops are straight-forward and intuitive,

but for complex systems or challenging

single controllers, RGA resulting from an

improved multivariable data analysis (PAT

application) improves control significantly.

Model Predictive Control

Model Predictive Control (MPC) was devel-

oped to solve control challenges of complex

dynamics, multivariate interactions and

direct quality control. For example, an MPC

solution was very early industrially deployed

to solve challenges in on-line optimization

projects in hydrocarbon plants. The plant

operator agreed that the mathematically

calculated optimum was probably the best

operating condition, but that he could not

get the plant from where it was to the

optimum. His challenge was the large

number of operating constraints and trip

limits that prohibited success. Let us

investigate each control challenge solved

by MPC individually.

Some processes have long, complex

and/or mixed dynamic interactions. As an

example, most spray dryers are controlled

rapidly by adjusting feed rate of concen-

trate to the dryer. These fast dynamics are

significantly different than the slow

response required to stably adjust heated

drying air. For this reason, most dryers are

controlled with regulatory temperature

control by adjusting the dryer feed rate

(the fast response). Because the desired or

ideal control includes both fast (pump

speed) and slow (temperature) relation-

ships, MPC is required for the best-in-class

dryer control.

Other challenging dynamic problems occur

in very slow processes, where the process

responds hours or even days after a

control change is made. MPC mathematics

includes a long memory of the past

process dynamics and is designed to

manage a process in cascade to regulatory

control. Very slow processes can be

managed at an appropriate frequency.

As described in both the dryer and the

hydrocarbon example above, MPC uses a

multivariate mathematical model to calcu-

late and drive to optimal control action.

Two, three, or even tens of interacting

variables can be controlled in a coordi-

nated fashion. Variables include fully

W H I T E P A P E R

Figure 4

Reduced Moisture Variability, Shift Target With ControlFigure 3

Moisture Variability – Before Control

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Typical Dryer ControlFeed Density

Ambient HumidityUncontrolled Influences(Disturbance Variables)

Dryer Control

Process SetpointsManipulated Variables)

Dryer Pressure or Exhaust Fan Speed

Afterdryer Temperature

Primary Air Flow

Inlet Air Temperature

High PRessure Pump Speed

Dryer Constraints

Exhaust Gas Temperature

Exhaust Gas RH

Hopper Moisture

dynamic predictive models of the

controlled parameters, such as quality,

throughput and efficiency as well as a

variety of operating constraints or limits.

Another type of variable included in this

architecture is feed-forward control

variables or process disturbance indica-

tors. A model-predictive projection, or

variable trajectory, of what is calculated to

affect quality can respond to disturbance

variable changes and make corrective

controller action before quality is signifi-

cantly impacted. Disturbance variables

which are not taken into account in the

control architecture can significantly affect

the final product outcome, especially in

areas with considerable variability in

climate (i.e. humidity and temperature). In

the dryer example mentioned above,

where ambient humidity is measured, the

heater can be adjusted as a weather front

moves in and moistens the drying air. The

powder moisture is held fairly constant.

Control technology can be use across the

entire unit operations train for feed

forward and feedback control.

Finally, MPC has enabled direct control of

the process objectives: quality, throughput

and efficiency. Analytic technologies can

enable predictive quality models that can

be controlled based on inferential model

results and continuously biased to lab

quality results as they are measured.

Model-based controllers on composition,

moisture content, density and pH have all

been successfully deployed. Instead of

controlling stream flows or temperatures,

tighter quality is achieved simply by

directly controlling the process objectives.

This is a direct utilization of the results of

verified mathematical models. MPC can

include multiple variables in complex

dynamic and static relationships for direct

digital control of quality, of inferred labora-

tory results or on-line analyzers.

Where PAT Makes Sense

There has been much discussion on what

PAT is and what types of applications can

be done under the PAT initiative. But how

does a company make an assessment of

where to look within its own operations to

apply a PAT implementation? And, how can a

company best apply a PAT implementation to

improve quality and operations performance?

Many manufacturers have asked whether a

PAT project should be attempted on a new or

existing product/process. This depends on a

number of factors. The answers to the

questions below can serve as a good

starting point in determining if a PAT

project should be initiated. Many of these

questions will need to be analyzed in

parallel, not in series. Which factors are

more important will change based on the

process, product and company objectives.

W H I T E P A P E R

Figure 5

Dryer Controller

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Is the process well understood?

If the process is not fundamentally under-

stood, the first step is to look at offline

multivariable data analysis to see whether

current available information can provide

useful information when analyzed with data

analysis tools. Multi-variable data mining

and modeling tools are available to provide

significant insight and shed light on complex

multivariable relationships to real-world

problems. Multiple case studies are also

available to assist in this assessment. Once

the process is well understood, then existing

models can be used to develop an offline

supervisory model providing "what-If

analysis" capabilities based on process

changes or production objectives, an online

predictive software sensor providing real-

time monitoring and predictions of key

variables that affect the process, or an online

advanced process control application that

continuously drives the process towards

desired objectives.

Is there significant variability in quality or

yield targets?

The goal of PAT is to understand the source

of process variability and reduce it.

Advanced process control shares the same

goal. Processes that display swings in

quality, exhibit an inability or long time to

recover within control specifications once

an upset occurs, or produce different results

with each production cycle represent great

candidates for a PAT initiative. The key for

a successful PAT project is to incorporate

process design, analysis and control into

the final project. By reducing variability

with analysis and control, pharmaceutical

companies have the potential to success-

fully realize significant, measurable value.

If so, is the source of variability quantified

or unknown?

In processes where there are a number of

known or unknown factors affecting quality,

a complete multivariable, and likely non-

linear, approach will need to be made or at

least investigated. In this case, an MPC

approach will be the recommended path to

achieve the highest quality and reduced

variability. For example, the need for a

real-time measurement of a key quality

parameter, a highly multivariable, non-

linear MPC approach might be reduced to

the addition of an analyzer to provide real-

time quality information online, at-line or

inline to the operator who would make

appropriate adjustments.

Where is the product in its lifecycle

(clinical trials, production, off-patent, etc.)?

There are benefits of deploying a PAT project

on both existing and new products. In

general, most new products have longer

lifecycles than products already in produc-

tion. In addition, new products can incorpo-

rate process analytical technology (PAT) and

validation protocol into the initial production

design. This has potentially significant finan-

cial benefits year-over-year. However, existing

products with strong market positions can

achieve significant profitability improvement

by a step change in increased yield and also

present a good candidate for PAT project. The

benefits are further magnified where existing

products are being made in similar or

identical production lines or facilities.

Would a PAT implementation be justified

for this product?

Most investments in new processes or

technologies require a financial analysis to

provide the company with a solid cost-

benefits profile. Most PAT projects follow a

similar approach. Areas where companies

will see significant financial return include:

> Quality improvements as a result ofreduced off-spec product and an abilityto operate closer to target specification;

> Production improvement that leads tohigher yields and/or more throughput;

> Reduced time-to-market;

> More efficient production whichreduces energy and raw material useper product produced;

> Improved production flexibilitythrough PAT implementation;

> Potential for a reduction in validationcosts and compliance; and

> Potential for flexible post-approvalcontinuous improvement activities.

W H I T E P A P E R

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In addition, there are other possible benefits

that are process or manufacturer-specific

that could add to the ultimate economic

value provided by a particular PAT project

(i.e. bringing a key product to market sooner

than a competitor). As in any project, the

PAT implementation costs are balanced

against the lifecycle value provided by that

initiative. Companies in a variety of indus-

tries have realized significant return on

investment and recurring value year-over-

year through multivariate data analysis,

online monitoring and control projects.

Conclusion

The Process Analytic Technology (PAT) initia-

tive provides pharmaceutical manufacturers

with an opportunity to use the latest avail-

able technology to better understand and

control process variability. Given a better

understanding of what variables impact

quality, efficiency and performance, PAT

provides a framework for manufacturers to

reduce regulatory constraints, enhance

product quality, reduce variable costs, and

increase yield.

How a PAT implementation is applied can

be assessed effectively through a series of

requirements ranging from conducting a

baseline of current process understanding

to evaluating the financial improvement

delivered during a project. A manufacturer's

key business objectives should dictate what

products and performance indices to

consider when identifying a PAT project.

Using an updated control strategy may not

be the ultimate result of every PAT project,

but many will conclude that the most direct

path to process and quality improvement is

to update the process control strategy based

on leveraged use of PAT discoveries.

Evaluations and alternatives to such conclu-

sions are presented here. In summary, PAT

enables manufacturers to turn data into

knowledge, knowledge into action, and

action into real business value.

About the Authors

Michael Tay (mtay@pavtech.com) is a

Technical Account Manager with Pavilion

Technologies, where he is involved in

evaluation and development of novel

industrial applications in optimization and

control. With almost 20 years of experi-

ence developing and deploying energy

and production optimization projects,

Michael has focused the last three years

on dry- and wet-milling, drying and

pharma pilot projects. He holds an M.Sc. in

Chemical Engineering and a B.Sc. in

Biochemistry.

Dennis Williams (dwilliams@pavtech.com)

is Director of Pavilion Technologies

pharmaceutical solutions, helping pharma-

ceutical manufacturers deploy Process

Analytical Technology (PAT) projects to

realize significant value. Working with

leaders in the food, pharmaceuticals and

electronics markets, Dennis has over 20

years of experience in the field of

advanced computer control and process

automation software. He has spent the last

8 years working on leading edge software

and advanced process control applications

in the pharmaceutical market. He holds a

degree in Nuclear Engineering

Technologies from Penn State University.

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© 2005 Pavilion Technologies, Inc. All rights reserved. Pavilion and Pavilion Technologies are registered trademarks in the U.S.patent and Trademark Office. Pavilion8 and ValueFirst are U.S. trademarks. All other names are trademarks of their respectivecompanies. (PAV535-11/05)

About Pavilion Technologies, Inc.

Pavilion Technologies, a leader in the field of advanced process control (APC), works with toppharmaceutical manufacturers to demonstrate PAT benefits through the use of multivariable dataanalysis, software online analyzers, and model predictive control. Pavilion uses a ValueFirst™

customer engagement methodology to identify the economic value of each PAT initiative andguarantee results based on the performance benchmark.

For more information, contact dwilliams@pavtech.com or call 610.222.9181.