Coupling Process Control Systems and Process Analytics to Improve Batch Operations

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Coupling Process Control Systems and Process
Analytics to Improve Batch Operations

• Bob Wojewodka, Technology Manager
• Philippe Moro, Global IS Manager
• Terry Blevins, Principal Technologist

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Presenters

• Robert Wojewodka
• Philippe Moro
• Terry Blevins

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Introduction

• 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 ltgt Emerson alliance and
  collaborative work to advance these concepts
• The beta test field trials

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A Premier Specialty Chemical Company
The Lubrizol Corporation

• Building 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

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Production in Lubrizol

• Predominantly batch
• Some continuous
• Full spectrum of automation
• Diversity in control systems
• Both reaction chemistry blending
• On-line and off-line measurement systems

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Production 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

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Lubrizol / Emerson Alliance

• 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

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Analytics Drive the Power of Information
Optimization
The Power of Information
ROI
Predictive Modeling
Descriptive Modeling
What is the Best that could happen?
Ad hoc Reports OLAP
What will happen?
Standard Reports
Raw Data
Why did it happen?
What happened?
Adapted by RAWO from slide courtesy of SAS Inst.
Data
Information
Knowledge
Intelligence
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3 Levels of Analytics Identified In Lubrizol
Data Analytics
Off-Line
On-Line
Routine data access
Via a Web Page

• 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.

• 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

Applications
Clients
Clients
Clients
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The Challenge of Batch Operation

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

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The setting

• 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.

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The Nature Of Batch Data is also a challenge
Each batch has data vectors
Time
Batch 1
Batch 2
Batch 3
Batch 4
Batches
Batch 5
Batch 6
Y - Space
Batches all have variable length time durations
Batch .. bi
X - Space
Quality Measurements
On-line Process Measurements
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The Nature Of Batch Data is also a challenge
Within between batch analysis
Within Batch Variation
Operation Oi, Phases 1 to pi
Operation 3, Phases 1 to pi
Operation 2, Phases 1 to pi

• 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

Operation 1, Phases 1 to pi
Batch 1
Batch 2
Batch 3
Between Batch Variation
Batch 4
Batch 5
Batch 6
Y - Space
Batch .. bi
X - Space
Quality Measurements
On-line Process Measurements
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What 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

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Also a need to move beyond univariate thinking
Univariate SPC Charts
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The need to move beyond univariate thinking
Variable 2
Variable 1
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The need to move beyond univariate thinking
Multivariate SPC Chart
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But, there are many more than 2 process variables
X2
This relationship of X1, X2, and X3 explains
the next highest amount of the variation between
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
Measurements across many batches for these three
variables. We now have a swarm of points in
three dimensions.
X1
Principal Component 1
Principal Component 2
X3
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The analysis reduces this complexity
This then allows us to simplify these complex
process relationships to a much lower dimension
that we can use, interpret, and exploit.
The observations may be projected onto a plane.
Principal Component 2
Principal Component 1
The analysis extends beyond 3 variables to k
variables (k dimensional space)
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Extending the analysis to correlate process
relationships with outputs (the Y-space)
Each observation in the x-space corresponds to a
measured result in the y-space
Multivariate process relationships defined (PCA)
Final batch quality and output relationships
defined (PLS PLS-DA)
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Batch 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.

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We 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.
• Emersons 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 LubrizolltgtEmerson alliance, we are
  leveraging these areas of expertise to bring the
  on-line analytics to a reality.

Preparing For Field Trials
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Summary 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

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The Multivariate Analyses Being Used

• 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

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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!

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For this to work, there needs to be a standard
architecture
SAP
Analysts Chemists Engineers
Analysis servers
Recipe Schedule
.net Web services
Consumption
XML
Data Transfer
Data Transfer
via XML
Embedded Analysis
Operator interface
Batch Exec.
Pro
Data Historian
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A strong level of security to protect the flow
of data
Microsoft Internet Information Service Server
Lubrizol Web Service
Lubrizol Windows Service
Lubrizol Domain
Firewall
Delta V Domain
XML
Microsoft Internet Information Service Server
Lubrizol Web Service
Lubrizol Windows Service
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Integration with data analytics
SQL Batch Historian data
Web Client
Continuous Historian data
Analysis servers
Tags

• Makes the marriage (matrix) between
  Batch/Tags/Lab
• Utilizes Web services
• Organizes this for easy access from web client

Lab Data -Device -Manual
DeltaV Systems
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Analytics for Batch Processes
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Model 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.

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Support for Process Analytics
History Collection of Lab and Spectral Analyzer
Data
Processing of Sample Data for Use in Analytics
Controller
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Operator Interface for Beta Test
PLS Quality Parameter Prediction
PCA Fault Detection
Contribution Plot
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Planned 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

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Beta Installation
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Summary

• 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!

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Where 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 Robert.Wojewodka_at_lubrizol.com
• Philippe Moro Philippe.Moro_at_lubrizol.com
• Terry Blevins Terry.Blevins_at_EmersonProcess.com
• At the 2008 Emerson Users 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