Terry Blevins

Principal Technologist

Emerson Process Management

Austin, TX

On-line Decision Support for Operations Personnel

Product quality predictions

Early process fault detection

Embedded On-line Analytics brings quality information, fault

detection, and abnormal situation knowledge to the operator –

bridging the gap between quality and control.

The PAT Guidelines issued by the FDA emphasized the use of

multivariate analytics as a means of reducing cost, improving

product quality.

QUALITY CONTROL

PCA – Principal Components Analysis

Provides a concise overview of a data set. It is powerful for

recognizing patterns in data: outliers, trends, groups,

relationships, etc.

PLS – Projections to Latent Structures

The aim is to establish relationships between input and output

variables and developing predictive models of a process.

PLS-DA – PLS with Discriminant Analysis

When coupled, is powerful for classification. The aim is to

create predictive models of the process but where one can

accurately classify future unknown samples.

Through the use of Principal Component Analysis

(PCA) it will be possible to detect abnormal

operations resulting from both measured and

unmeasured faults.

Measured disturbances – may be quantified through

the application of Hotelling’s T2 statistic.

Unmeasured disturbances – The Q statistic, also

known as the Squared Prediction Error (SPE), may be

used.

Faults are determined by comparing these

calculated statistics to an upper limit

An abnormal condition is indicated if the value exceeds

the limit.

Web services and OPC

Busses

Integrated Order and Campaign Information / Visualization

On-line Data Analytics

Lab Info Mgmt

Analyzer Management

Process Automation & Asset Management

Machinery Manager

Batch Control and Historian

Analyzers

Production Management

Instrumentation Machinery

Process holdups. Tools must account for operator and event initiated processing halts and restarts.

Access to lab data. Lab results must be available to both the off-line and the online analytic toolsets.

Variations in feedstock. The properties associated with each material shipment should be available for use in online analytic tools.

Varying operating conditions. The analytic model must account for the batch being broken into multiple operations that span multiple units.

Concurrent batches. The data collection and analysis toolset and online operation must take into account concurrent batches.

Assembly and organization of the data. Efficient tools to access, correctly sequence, and organize a data set must be available to analyze the process and to move the results of that analysis online.

Time

Bat

ches

Y - Space

On-line Process Measurements

QualityMeasurements

X - Space

Batch 1

Batch 2

Batch 3

Batch 4

Batch 5

Batch 6

Batch .. biBatches all have variable length time durations

Dynamic Time

Warping

A key technology

used in model

generation

Feature matches

dissimilar length

batches to a uniform

length for analysis

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109

Before

After

ISA S88.01 defines stage

as: “a part of a process

that usually operates

independently from other

process stages and that

usually results in a planned

sequence of chemical or

physical changes in the

material being processed”

Analytic models are defined

based on the batch stage –

consistent with ISA S88

model.

The inputs and outputs

used in analysis may be

different for each stage.

When creating PCA and PLS models for a selected product, the X variables are defined by stages.

Some X variables only apply to certain stages of manufacturing

Define X variable per stage using stage selection

Form a multi-discipline

team that includes plant

operations

Integrate Lab and

Truck Shipment Data

Identify Calculated Properties

Instrumentation Survey , tune control loops

Conduct Formal operator training

Capture team input e.g.

“input-output” data matrix

Analytic Process Models

Evaluation process operation

Process measurements, lab and Truck analysis over last year

Calculated Feed Composition

Process measurements

Operator Interface

Predicted End of Batch Quality

Fault Detection

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Storage Tank Design

Tank

Design 2Tank Design 1

Tank

Design 3

Storage Tank Design

Tank

Design 2Tank Design 1

Tank

Design 3

1. If either Fault Detection plot

exceeds or approaches the

upper control limit of 1.0, click

on that point in the trend and

-> Select the Parameter in the

lower corner of the screen that

contributed to the fault

2. Evaluate the parameter trends

from process operation

standpoint

-> take corrective action if

necessary

3. Inspect impact of fault on

quality prediction plot to find out

how quality may be affected

If a fault is indicted in the overview screen, then selecting the batch number will bring up the Fault Detection view.

Analytics Overview

Quality Parameter

Prediction

Contribution Parameter Trend (s)

Fault Detection

1

2

3

When the hot oil

valve is opened,

the flow rate is

much lower than

normal

The lower flow

rate impacts the

time needed for

the mixer to

reach target

temperature –

extending batch

time

Fault shows up in

Indicator 2

deviating above 1.

To find the cause of

the fault, select the

point of maximum

deviation and then

choose the

Contribution Tab or

select the

parameters that

contribute most to

the fault - shown in

the lower corner of

the screen.

The trend

confirms that

the media flow

rate is ~ 2

liters/sec which

is much lower

than the

normal flow

rate of 4

liters/sec.

The prediction

plot confirms

that the low oil

flow rate has

no impact on

the predicted

product

density.

For the Saline process, the prediction of product density has proven to be very accurate even though variations in the salt bin level are a major source of variation in the processing conditions.

Development of a bridge in the salt bin will reduce the flow of salt to the screw feeder and thus will impact the final product concentration

The reduced salt flow is reflected in the change in mixer level when the screw feeder is turned on.

The reduced salt flow when a bridge develops in the salt bin is detected as an unexplained deviation.

Reduced salt

flow is

reflected by a

less than

normal

change in

mixer level

while the salt

feeder turned

on.

Coating of the sensor may introduce a bias into the pH measurement -resulting in a shift of the pH maintained in the reactor.

May impact cell growth rate and product formation.

Fault shows up as an explained and unexplained change – deviation above 1.

To find the cause of the fault, select the point of maximum deviation and then choose the Contribution Tab.

Drift in the pH

measurement is

reflected in the

pH measurement

and controller

output.

A trend of the pH

and pH controller

output can be

obtained by

clicking on media

flow parameter in

the contribution

screen.

Impact of the

change in

measurement

bias is shown as

an immediate

change in pH.

Longer term the

faulty pH

measurement is

reflected in an

abnormally low

reagent addition

being used to

maintain the

indicated pH.

TT

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Coolant return

Bioreactor

RSP

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205AT

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FC

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FC

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FT

201Feed

e.g. Glucose

AC

204

Reagent

e.g. Ammonia

FC

202

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pH

AC

205Dissolved

Oxygen

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PT

208

PC

208

RSPCharge

e.g. Media

FT

203 Coolant

supply

IT

209

LT

210

To Harvest

TT

207

TC

207

TC

207

TT

206

TC

206

TC

206

Coolant return

Bioreactor

RSP

AT

205AT

204

FC

203

FC

203

FC

201

FC

201

FT

201Feed

e.g. Glucose

AC

204

AC

204

Reagent

e.g. Ammonia

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202

FC

202

FT

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pH

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AC

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e.g. Media

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supply

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To Harvest

Outputs

Inputs

Unit 1Unit 2

Outputs

Inputs

RM1 add - T setting

RM2 add and hold

Drying

Cartridge filtration

RM1 add - T setting

1st RM2 add and heat

2nd RM2 add and hold

Recover

Filter/adjust

Transfer

TT

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TT

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Coolant return

Bioreactor

RSP

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204

FC

203

FC

201

FT

201Feed

e.g. Glucose

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204

Reagent

e.g. Ammonia

FC

202

FT

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pH

AC

205Dissolved

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PT

208

PC

208

RSPCharge

e.g. Media

FT

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supply

IT

209

LT

210

To Harvest

TT

207

TC

207

TC

207

TT

206

TC

206

TC

206

Coolant return

Bioreactor

RSP

AT

205AT

204

FC

203

FC

203

FC

201

FC

201

FT

201Feed

e.g. Glucose

AC

204

AC

204

Reagent

e.g. Ammonia

FC

202

FC

202

FT

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pH

AC

205

AC

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PT

208

PC

208

PC

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RSPCharge

e.g. Media

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supply

IT

209

LT

210

To Harvest

The beta test showed through several batches an increasing deviation of the

density measurement of a critical component.

This phenomenon was linked to the start of plugging which was quickly solved

by applying steam without time cycle impact.

The on-line tool indicated a problem going on the cooling system of the reactor

It detected that the component charge was being introduced too slowly and that

the reactor temperature was running a little bit higher. The problem was solved

on the Aero cooler.

These problems were going unnoticed with the existing monitoring and control systems.

Problem identified with an “up stream” boiler negatively impacting

operations. A quote from the operations personnel follows:

“…thanks to the Beta. An equipment failure was discovered in advance and avoid

losing 5 hours per batch for the batch in process and also for the following

batches before discovering the problem with the traditional manner. Probably

some days would have be necessary to discover that type of mechanical problem

without the Beta. (Boiler combustion air controller located in a bad accessible

zone and thermal oil leakage). …(we would have) discovered this latter with the

periodic update of the indicators of efficiency, but we saved time earlier thanks to

the beta. Earlier is better than too late!”

The use of on-line batch data analytics will cause people to think

in entirely new ways and to address process improvement and

operations with a better understanding of the process.

Its use will allow operational personnel to identify and make well-

informed corrections before the end-of-batch, and help ensure that

batches repeatedly hit pre-defined end-of-batch targets.

Use of this methodology with allow engineers and other operations

personnel to gain further insight into the relationships between

process variables and their impact on product quality parameters.

It also will provide additional information to help process control

engineers pinpoint where process control needs to be improved.

The results

Greater understanding of the process

An increase in quality consistency

Increased throughput

More good batches!

Published Papers

Robert Wojewodka and Terry Blevins, “Data Analytics in Batch Operations”, Control, May 2008

Terry Blevins and James Beall, “First Steps to Address PAT Initiative”, Pharmaceutical Canada, Volume 8 Number 4, March-April, 2008

Conference Presentations

Robert Wojewodka, Terry Blevins, “Benefits Achieved Using On-Line Data Analytics”. Emerson Exchange, 2009

Terry Blevins, “Overview of PAT and Application of PAT for Product Development”, Life Science Session, Emerson Exchange, 2008

Robert Wojewodka, Willy Wojsznis, “Process Analytics In Depth”, Emerson Exchange, 2008

Robert Wojewodka, Terry Blevins, “The Application of Data Analytics in Batch Operations”, Emerson Exchange, 2008

Michel Lefrancois, Randy Reiss, “Tools for Online Analytics”, Emerson Exchange, 2008

Conference Presentations (Cont)

Terry Blevins, Michael Boudreau, Yan Zhang, Trish Benton, “Application of PAT in Product Development”, Interphex2008 – Conference Presentation, March, 2008

Philippe Moro, Christopher Worek, “Integrating SAP® Software into DeltaV”, Emerson Exchange, 2008

Robert Wojewodka, Philippe Moro, Terry Blevins, “Coupling Process Control Systems and Process Analytics to Improve Batch Operations”, Emerson Exchange Presentation, 2007

Video

Video: Scott Broadley, Trish Benton, Terry Blevins, Emerson - BroadleyJames Beta: http://www.controlglobal.com/articles/2007/309.html .

Video: Robert Wojewodka, Philippe Moro, Terry Blevins Emerson - Lubrizol Beta: http://www.controlglobal.com/articles/2007/321.html

Books

Michael Boudreau and Gregory McMillan, New Directions in Bioprocess Modeling and Control , Chapter 8, ISA, 2006