coupling process control systems and process analytics to improve batch operations bob wojewodka,...

Coupling Process Control Coupling Process Control Systems and Process Systems and Process Analytics to Improve Batch Analytics to Improve Batch OperationsOperations

Bob Wojewodka, Technology Manager

Philippe Moro, Global IS Manager

Terry Blevins, Principal Technologist

[File Name or Event]Emerson Confidential27-Jun-01, Slide 22

PresentersPresentersPresentersPresenters

• Robert Wojewodka

• Philippe Moro

• Terry Blevins


IntroductionIntroductionIntroductionIntroduction

• What we will cover:– The need to move beyond process control to

process data analytics coupled with control

– Why this is important

– Basic concepts on the analysis methodology

– The Lubrizol <> Emerson alliance and collaborative work to advance these concepts

– The beta test field trials


A Premier Specialty Chemical CompanyA Premier Specialty Chemical CompanyA Premier Specialty Chemical CompanyA Premier Specialty Chemical CompanyBuilding on our “special chemistry,” a unique blend of people,

processes and products, Lubrizol:

• Provides innovative technology to global transportation, industrial and consumer markets

• Pursues our growth vision to become one of the largest and most profitable specialty chemical companies in the world

A special chemistry aligned for financial successA special chemistry aligned for financial success

The Lubrizol CorporationThe Lubrizol CorporationThe Lubrizol CorporationThe Lubrizol Corporation


• Predominantly batch

• Some continuous

• Full spectrum of automation

• Diversity in control systems

• Both reaction chemistry & blending

• On-line and off-line measurement systems

Production in LubrizolProduction in LubrizolProduction in LubrizolProduction in Lubrizol


Production ChallengesProduction ChallengesProduction ChallengesProduction Challenges• Addressing the required batch data structures

• Better addressing process relationships

• Characterizing process relationships sooner

• Identifying abnormal situations / events sooner

• Better relating process relationships to end process quality and economic parameters

• Moving process data analytics “on-line”

• Continual improvement of Operational Excellence


• Alliance agreement

• Pricing

• Conversion to DeltaV / many projects

• Standardize on aspects of PlantWeb architecture

• Collaboration

– Exchanging process optimization and data analysis, and integration knowledge with Emerson

– Emerson sharing knowledge with Lubrizol

– Collaborative development projects

– Lubrizol committed to assist with field trials and be early adopters

Lubrizol / Emerson AllianceLubrizol / Emerson AllianceLubrizol / Emerson AllianceLubrizol / Emerson Alliance


The Power of Information

Raw Data

StandardReports

Descriptive Modeling

PredictiveModeling

Data Information Knowledge Intelligence

Optimization

What happened?

Why did it happen?

What will happen?

What is the Best that

could happen?$$$

ROI

$$$

ROI

Adapted by RAWO from slide courtesy of SAS Inst.

22

Ad hoc Reports& OLAP

Analytics Drive Analytics Drive the Power of Informationthe Power of Information

Analytics Drive Analytics Drive the Power of Informationthe Power of Information


3 Levels of Analytics Identified 3 Levels of Analytics Identified In LubrizolIn Lubrizol

3 Levels of Analytics Identified 3 Levels of Analytics Identified In LubrizolIn Lubrizol

Routine & data access

Routine & data access Off-LineOff-Line On-LineOn-Line

Data AnalyticsData Analytics

ClientsClientsApplicationsApplications

• Routine analyses• Routine reports• Routine graphical

summaries• Routine metrics & KPIs• Vehicle for data selection

by user• Vehicle to deliver data to

the user• On-line visualization

• Add hoc analyses• Model development• Process studies• Lab studies• Business studies• Troubleshooting• Process improvement• Interactive analyses• …etc.• People do their own

analyses using the analysis tools

• Real-time analytics• Deployment of models• ASP analytics• Process analytics• Monitoring, feedback,

control, alerts• Link back into PlantWeb• Web interface for the

display• Etc.

ClientsClients ClientsClients

Via a Web Page


The Challenge of Batch OperationThe Challenge of Batch OperationThe Challenge of Batch OperationThe Challenge of Batch Operation

The wide range of operating conditions present challenges in the design, commissioning, and on-going manufacturing.

TT 207

TC 207

TT 206

TC 206

Coolant return

Bioreactor

RSP

AT 205AT

204

FC 203

FC 201

FT 201

Feed e.g. Glucose

AC 204

Reagent e.g. Ammonia

FC 202

FT 202

Air

pH

AC 205

DissolvedOxygen

Vent

PT 208

PC 208

RSPCharge e.g. Media

FT 203 Coolant

supply

IT 209

LT 210

To Harvest

TT 207

TC 207TC 207

TT 206

TC 206TC 206

Coolant return

Bioreactor

RSP

AT 205AT

204

FC 203FC 203

FC 201FC 201

FT 201

Feed e.g. Glucose

AC 204AC 204


FC 202FC 202

FT 202

Air

pH

AC 205AC 205

DissolvedOxygen

Vent

PT 208

PC 208PC 208


FT 203 Coolant

supply

IT 209

LT 210

To Harvest


TT 207

TC 207

TT 206

TC 206

Coolant return

Bioreactor

RSP

AT 205AT

204

FC 203

FC 201

FT 201

Feed e.g. Glucose

AC 204


FC 202

FT 202

Air

pH

AC 205

DissolvedOxygen

Vent

PT 208

PC 208


FT 203 Coolant

supply

IT 209

LT 210

To Harvest

TT 207

TC 207TC 207

TT 206

TC 206TC 206

Coolant return

Bioreactor

RSP

AT 205AT

204

FC 203FC 203

FC 201FC 201

FT 201

Feed e.g. Glucose

AC 204AC 204


FC 202FC 202

FT 202

Air

pH

AC 205AC 205

DissolvedOxygen

Vent

PT 208

PC 208PC 208


FT 203 Coolant

supply

IT 209

LT 210

To Harvest

• Operators work in a highly complex, highly correlated and dynamic environment each day. Any help with advanced warning of pending events is valuable.

• Operators manage a large amount of data and information on a running unit. Even with automated units, only so much can be monitored and managed at one time. Any help with automatic monitoring across many variables is valuable.

• The ultimate goal is to prevent the undesirable effects of an abnormal situation by early detection or the detection of a precursor to an undesirable event.

The setting:The setting:The setting:The setting:


Each batch has data vectorsEach batch has data vectorsTime

Ba

tch

es

Y - Space

On-line Process Measurements

QualityMeasurements

X - Space

Batch 1

Batch 2

Batch 3

Batch 4

Batch 5

Batch 6

Batch .. bi

Batches all have variable length time durations

The Nature Of Batch Data is The Nature Of Batch Data is also a challengealso a challenge



Y - Space

On-line Process Measurements

QualityMeasurements

X - Space

Batch 1

Batch 2

Batch 3

Batch 4

Batch 5

Batch 6

Batch .. bi

Operation 1, Phases 1 to pOperation 1, Phases 1 to piiOperation 1, Phases 1 to pOperation 1, Phases 1 to pii



Operation OOperation Oii, Phases 1 to p, Phases 1 to piiOperation OOperation Oii, Phases 1 to p, Phases 1 to pii

Within & between batch analysisWithin & between batch analysisWithinWithinBatchBatchVariationVariation

BetweenBetweenBatchBatchVariationVariation

Analyze data in order to:

• Decrease costs

• Reduce time cycle

• Reduce processing problems

• Increase yield

• Improve quality

• Reduce waste

• Reduce variability

• Improve reliability

• Avoid undesirable upsets




What is neededWhat is neededWhat is neededWhat is needed• Process analysis (off-line, on-line), identify:

– Process relationships

– Influential parameters

– Correlation to quality parameters

– Correlation with economic parameters

• Alarming for operators and focused advise

• Process assessment and control– Monitoring process performance

– Detection of upsets

– Finding assignable causes

– Early detection

– Drill down for explanation of deviations

– Actions taken and feedback


Also a need to move beyond Also a need to move beyond univariate thinkingunivariate thinking

Also a need to move beyond Also a need to move beyond univariate thinkingunivariate thinking

SPC Chart for Variable 1

0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907UCL = 96.5239

LCL = 83.6576


0 10 20 30 40 50 60

Observation

0

2

4

6

8

10

12

X

CTR = 5.9426UCL = 11.5478

LCL = 0.3374

Univariate SPC ChartsUnivariate SPC ChartsUnivariate SPC ChartsUnivariate SPC Charts



0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907UCL = 96.5239

LCL = 83.6576

SP

C C

hart

for

Variable

2

010

20

30

40

50

60

Observ

ation

0246810

12

X

CT

R =

5.9

426

UC

L =

11.5

478

LC

L =

0.3

374

Control Ellipse

82 86 90 94 98

Variable 1

-1

2

5

8

11

14

Varia

ble

2

Variable 1

Var

iabl

e 2

The need to move beyond The need to move beyond univariate thinkingunivariate thinking



Multivariate SPC ChartMultivariate SPC ChartMultivariate SPC ChartMultivariate SPC ChartMultivariate Control Chart

UCL = 10.77

0 10 20 30 40 50 60

Observation

0

4

8

12

16

20

24

T-S

quare

d




But, there are many more than 2 process variables

X1

X2

X3

Even though in 3-dimensions, this

relationship made up of X1, X2 and X3 is the one that

“explains” the highest amount of

the variation between the

batches

Even though in 3-dimensions, this

relationship made up of X1, X2 and X3 is the one that

“explains” the highest amount of

the variation between the

batches

PrincipalComponent

#1

PrincipalComponent

#2

Measurements across many

batches for these three variables.

We now have a “swarm” of points

in three dimensions.

Measurements across many

batches for these three variables.

We now have a “swarm” of points

in three dimensions.

This relationship of X1, X2, and X3

“explains” the next highest amount of

the variation between batches

This relationship of X1, X2, and X3

“explains” the next highest amount of

the variation between batches


The observations may be “projected”

onto a plane.

The observations may be “projected”

onto a plane.

Principal Component 1

Principal Component 2

PC1

PC2

The analysis extends beyond 3 variables to k variables (k dimensional space)

This then allows us to simplify these complex process relationships to a much lower dimension that we can use, interpret, and exploit.

This then allows us to simplify these complex process relationships to a much lower dimension that we can use, interpret, and exploit.

The analysis reduces this complexity


Extending the analysis to correlate process relationships with outputs (the Y-space)

X1

X2

X3

VariabilityFactor 1

VariabilityFactor 2

Y1

Y2

Y3

VariabilityFactor 1

VariabilityFactor 2

Each observation in the x-space corresponds to a

measured result in the y-space

Each observation in the x-space corresponds to a

measured result in the y-space

Multivariate process

relationships defined (PCA)

Multivariate process

relationships defined (PCA)

Final batch quality and output

relationships defined(PLS & PLS-DA)

Final batch quality and output

relationships defined(PLS & PLS-DA)


Batch Analytic ChallengesBatch Analytic ChallengesBatch Analytic ChallengesBatch Analytic Challenges Linking data with the product identifier Operation / phase / state data from the unit Many sources of data need to be combined, not all is within

DeltaV Dimensionality (large in both the x-space & y-space) Collinearity and autocorrelation Noise and missing data Multivariate relationships are prevalent Addressing process dynamics Having access to historical data On-line requirements and on-line challenges The need for dynamic time warping Etc.


We Feel We Have a SolutionWe Feel We Have a SolutionWe Feel We Have a SolutionWe Feel We Have a Solution• Lubrizol has expertise and a long standing use of multivariate

data analysis in support of off-line process characterization and process improvement activities.

• Emerson Process Management also established a research project at University of Texas (UT), Austin in September, 2005 to investigate advanced process analytics.

– The primary objective of this project is to explore the on-line application of Analytics for prediction and fault detection and identification in batch operations.

– Emerson’s research grant given to UT is funding the work of a PhD graduate student, Yang Zhang, under the supervision of Professor Tom Edgar.

• Through the Lubrizol<>Emerson alliance, we are leveraging these areas of expertise to bring the on-line analytics to a reality.

Preparing For Field Trials


Summary of ResearchSummary of ResearchSummary of ResearchSummary of Research

There are many texts available on these topics

Also reference chapter 8 of the book “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”


• 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. Also used to help put “atypical” process variation into a context of what is important.

• 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

The Multivariate Analyses Being Used


Are these methods new?

• NO, they have been around and in use for quite some time

• These are proven methods for use for characterizing process relationships

• They are just new to some areas such as: • Pharmaceutical companies with their PAT initiatives • New to many long standing industries such as the process

automation and control and many others• New to certain fields of study such as engineering, process

engineering, control engineering, etc.

• Used very effectively in “off-line” process improvement studies

• Historically many limitations to move these methods “on-line”. But times have changed, we are now ready!


.net Web servicesSAP Process Order& Recipe

Consumption Data

Firewall

ResourceOptimization andPlanning Application

Batch Exec & Campaign Mgr

Historian &Recipe Exchange

PRO+Operator Interface

Recipe Transfer via XML

Consumption from Batch Historian event file via XML

Control Network

LZ Domain

SAP Analysis server(s)Analysts

Embeddedanalytics

Device level analysis / diagnostics

Device level analysis / diagnostics

Embedded analysis &

diagnostic apps.

Embedded analysis &

diagnostic apps.

Business & Process Analytics

Business & Process Analytics

Data Transfer via XML

Pro+

.netWeb services

Batch Exec.

Consumption

SAP

Data Historian

Operator interface

Data Transfer

Analysis serversAnalystsChemistsEngineers

EmbeddedAnalysis

XML

Recipe +Schedule

For this to work, there needs to be a standard architectureFor this to work, there needs to be a standard architectureFor this to work, there needs to be a standard architectureFor this to work, there needs to be a standard architecture


Firewall

Lubrizol Windows Service

Microsoft Internet Information Service Server Lubrizol Web Service

XML

A strong level of security A strong level of security to protect the flow of datato protect the flow of dataA strong level of security A strong level of security to protect the flow of datato protect the flow of data

Lubrizol Windows Service

Microsoft Internet Information Service Server Lubrizol Web Service

Lubrizol Domain

Delta V Domain


FirewallFirewall

Firewall

DeltaVSystems

Lab Data -Device-Manual

SQL Batch Historian data

• Makes the marriage (matrix) between Batch/Tags/Lab

• Utilizes Web services

• Organizes this for easy access from web client

WebClient

Tags

Continuous Historian data

Integration with data analyticsIntegration with data analyticsIntegration with data analyticsIntegration with data analytics

Analysis servers


Analytics for Batch ProcessesAnalytics for Batch ProcessesAnalytics for Batch ProcessesAnalytics for Batch Processes


Model Development – Aligning BatchesModel Development – Aligning BatchesModel Development – Aligning BatchesModel Development – Aligning Batches• Data for different

length of Batches is aligned using dynamic time warping

• The aligned data is processed using hybrid unfolding before using this to train the multi-way PCA or batch-wise unfolding for PLS/PLS-DA model.


Support for Process AnalyticsSupport for Process AnalyticsSupport for Process AnalyticsSupport for Process Analytics

History Collection of Lab and Spectral Analyzer Data

Controller

ModuleLab Results

Analytic Block

DeltaV Historian Operator Station

ProPlus Off-line Modeling

OtherData

Processing of Sample Data for Use in Analytics


Operator Interface for Beta TestOperator Interface for Beta TestOperator Interface for Beta TestOperator Interface for Beta Test

PCA – Fault Detection PLS – Quality Parameter Prediction

Contribution Plot


Planned Beta InstallationPlanned Beta InstallationPlanned Beta InstallationPlanned Beta Installation

• Demonstrate on-line prediction of quality and economic parameters

• Evaluate different means of on-line fault detection and identification i.e. multi-way PCA/PLS.

• Show value of high fidelity process models for testing fault detection and alternate control strategies.

• Discuss and explore extension of the methodologies into other aspects of the process unit data

• Refine the approach, user interfaces, and integration with other systems


Beta InstallationBeta InstallationBeta InstallationBeta Installation


SummarySummarySummarySummary

• Fieldbus Provides Infrastructure for Improved Diagnostic Capability

• Improvements Require New Capability in Field Devices and DCS Integration of These Features

• Multivariate statistical analysis methodology is needed to correlate relationships within and between devices with that of operational data

• Multivariate statistical analysis methodology is needed to correlate this with product quality and other parameters of interest

• The analyses need to be coupled with DeltaV

• The analyses need to be available for both off-line process studies as well as on-line process diagnostics and control

• Its Time to Implement!


Where To Get More InformationWhere To Get More InformationWhere To Get More InformationWhere To Get More Information

• Many excellent texts on multivariate analysis– Also reference: “New Directions in Bioprocess Modeling and Control:

Maximizing Process Analytical Technology Benefits”

• Bob Wojewodka: [email protected]

• Philippe Moro: [email protected]

• Terry Blevins: [email protected]

• At the 2008 Emerson User’s Conference– We will be presenting an overview of the field trials

• What was done

• What was found

• What were the benefits

• What are the next steps

coupling process control systems and process analytics to improve batch operations bob wojewodka,...

Documents

eventemerson confidential27

process quality

data analysis

process control systems

data selection

slide courtesy of sas

levels of analytics

special chemistry