Batch Process Analytics

- update -

Robert Wojewodka, Technology Manager and Statistician

Terry Blevins, Principal Technologist

Willy Wojsznis, Senior Technologist

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Presenters

Bob Wojewodka

Terry Blevins

Willy Wojsznis

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Introduction Lubrizol and Emerson Process Management

have worked together over the last three years to develop and install a beta version of Emerson’s on-line batch analytics

This new functionality is currently in operation after successful field trials at the Lubrizol, Rouen, France plant

In this session we will present the lessons learned in implementation of this technology in a running plant

We will also summarize the results achieved by the process operators and operations management using this new capability

We outline the basic principles and objectives of analytic application

We sketch some innovative analytic concepts which were validated at the field trial

Discuss current activities

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• Operators and engineers work in a highly complex, highly

correlated and dynamic environment each day

• Operators and engineers manage a large amount of data

and information on a running unit

• Operators and engineers need to avoid undesirable

operating conditions

• Operators an engineers need to reduce variation, improve

throughput and improve quality yet maintain safety

The Setting

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Jointly develop viable on-line multivariate batch process data analytics

The primary objectives of the field trial were:

– Demonstrate on-line prediction of product quality

– Evaluate different means of on-line process fault detection and identification; abnormal situations

Document the benefits of this technology

Learn from the field trial to update and improve these new and evolving modules

Objectives of the Beta TestObjectives of the Beta Test

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Process holdups

Access to lab data

Variations in feedstock

Varying operating conditions

Concurrent batches

Assembly and organization of the data

Challenges in Applying Online Data Analytics to Batch Processes

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Functionality of the Analytics Application

Take all inputs and process variables associated with a batch process and characterize “acceptable variation” and process relationships associated with “good” batches

Identify how these variables relate to each other and to end of batch product quality characteristics

Use the analytic techniques to identify typical process relationships and faults as current and future batches are running

Use the analytic techniques to predict end of batch quality characteristics at any point in time as a batch is evolving

Identify and diagnose faults and provide recommendations to operations personnel how to improve batch operation and product quality

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The “Golden Batch” comparison approach is plagued with problems

What is the “best” batch?

Only refers to ONE batch; but there are many “good” batches

Does not address fact that variation exists nor does it address defining an acceptable level of variability

May significantly miss direct resources

May significantly miss direct control emphasis

Does not promote process understanding nor does it promote identifying important process relationships; nothing is learned

The economics may be completely wrong

Does not promote identification and control of critical parameters and relationships with quality parameters (analytical, physicals, time cycle, yield, waste, economics, etc.)

…and the list goes on…

GoldenBatch

Comparison

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Analytics Drive the Power of Information

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 Bob Wojewodka from slide courtesy of SAS Inst.

Ad hoc Reports& OLAP

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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 and

diagnostic apps.

Business & Process Analytics

Business and 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

DeltaV and SAP Integration With Data Analytics

Statgraphics®

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Summary of Actual Field Trial Analyses

2 units / products 18 input variables 38 process

variables 4 output variables

(2 initially for the online)

All data at 1-minute time intervals for the analysis Total of 172 historical batches used for analysis and

model development across these two processes

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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 the material into a category.

The Primary Multivariate Methods

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Results of the Off-line Modeling Work

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Product 1Control screen

Analytics screen

Product 2Control screen

Operations personnel interact with the data analysis screen.

Other people from other locations / sites may access the on-line analysis displays via their web browser.

What Has Been Deployed

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What Has Been Deployed

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What Has Been Deployed

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What Has Been Deployed

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What Has Been Deployed

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Stage 1 Stage 2Stage 3

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Web-based Interface - There’s an App for that

Since the user interface is web-based it can be accessed from multiple sites over the intranet (or internet)

As will be demonstrated at the Rouen beta site, access is also available through an iPod Touch or iPhone.

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Summary of field trial results

Operators and engineers at Rouen are using these new tools for faults detection and quality parameter prediction.

The impact of the on-line analytic tools installed at Rouen on the plant operation have been evaluated over a 6 month period and since then the installation is in use beyond the initially planned period.

Examples of faults detected using this capability are provided in the presentation given at Emerson Exchange 2009 – see Benefits Achieved Using On-Line Data Analytics by Robert Wojewodka and Terry Blevins.

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Lessons Learned - Key concepts / approaches that have evolved from the beta work

Use of Stage in data analytics to define the major manufacturing steps

Selection and pre-processing of data used for model development and on-line analytics

On-line interface designed to meet operator’s requirements

Web based architecture for operator interface and data exchange

Development of a web based dynamic process simulation to enable effective operator training

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Current Activities

Emerson progressing with the commercialization of the batch analytics modules

– Will be part of the DeltaV Version 12 release

Following process improvement design changes on the field trial units, models will be updated and redeployed

Completion of a Design of Experiments to further characterize the modeling process relative to differing process relationships

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Design Of Experiments Examining more process relationships

and impacts on the analysis methods Results will be used to further refine the

modeling approach Results will be used for pre-assessment

of candidate units for use of the analysis modules

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Added Work Prior to Commercial Release

Off-line modeling tool set with enhanced diagnostics to aid the process engineers during model development steps

Ability to simultaneously predict multiple “Y” output variables while on-line

On-line diagnostics of the “health” of the running models; alert when model errors deviate beyond initial levels when deployed

Additional functionality for being able to update and redeploy models quickly following processing changes

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Where to Get More Information Interactive demonstration of data analytics applied to the saline process

http://207.71.50.196/AnalyticsOverview.aspx

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

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

Emerson Exchange 2010 Workshop – SAP to DeltaV integration using the DeltaV SOA Gateway and SAP Web Services – Philippe Moro, Joe Edwards, Chris Felts

Emerson Exchange 2009 Workshop – Benefits Achieved Using On-line Data Analytics - Robert Wojewodka, Terry Blevins

Emerson Exchange 2008 Short Course: 366 – The Application of Data Analytics in Batch Operations - Robert Wojewodka, Terry Blevins

Emerson Exchange 2008 Short Course: 364 – Process Analytics In Depth - Robert Wojewodka, Willy Wojsznis

Emerson Exchange 2008 Workshop: 367 – Tools for Online Analytics - Michel Lefrancois, Randy Reiss

Emerson Exchange 2008 Workshop: 412 – Integration of SAP® Software into DeltaV - Philippe Moro, Chris Worek

Emerson Exchange 2007 Workshop: 686 – Coupling Process Control Systems and Process Analytics to Improve Batch Operations – Bob Wojewodka, Philippe Moro, Terry Blevins

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Thank You

Q & A

Vision without action is merely a dream.

Action without vision just passes the time.

Vision with action can change the world.

--- Joel Barker, Futurist