The Application of Data Analytics in Batch Operations

1
The Application of Data Analytics in Batch
Operations

• Robert Wojewodka, Technology Manager and
  Statistician
• Terry Blevins, Principal Technologist

2
Presenters

• Robert Wojewodka
• Terry Blevins

3
Introduction

• Lubrizol Rouen project background and objectives
• Challenges of applying online analytics
• Beta project steps
• Collection of process information
• Integration of lab and tank property data
• Instrumentation and control survey
• Historian collection
• Model development
• Training
• Evaluation
• Summary
• More information - references

4
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

5
Lubrizols Production Facilities

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

6
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 online

7
Online Data Analytics

• 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 Hotellings T2 statistic.
• Unmeasured disturbances The Q statistic, also
  known as the Squared Prediction Error (SPE), may
  be used.
• Projection to latent structures, also known as
  partial least squares (PLS) may be used to
  provide operators with continuous prediction of
  end-of-batch quality parameters.

8
Online Data Analytics
9
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 established a research
  project at University of Texas Austin in
  September 2005 to investigate advanced process
  analytics.
• The primary objective of this project is to
  explore the online application of analytics for
  prediction and fault detection and identification
  in batch operations.
• Tools for PCA/PLS model development and online
  application have been developed.
• Through the LubrizolltgtEmerson alliance, we are
  leveraging these areas of expertise to bring the
  online analytics to a reality.

10
Rouen Beta Installation

• Collaborate on the development of Emersons tools
  for on-line prediction of process, quality and
  economic parameters

11
Challenges in Applying Online Data Analytics to
Batch Processes

• Process holdups. Tools must account for operator
  and event- initiated processing halts and
  restarts.
• Access to lab data. Lab results must be available
  to the online analytic toolset.
• Variations in feedstock properties associated
  with each material shipment should be available
  for use in online analytic tools.
• Varying operating conditions. The analytic model
  should account for 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 to analyze the process and to move the
  results of that analysis online.

12
Technical Advancements

• Two advancements enable batch analysis and online
  implementation of online analytics.
• A new approach known as hybrid unfolding offers
  some significant technical advantages in
  unfolding batch data for use in model
  development.
• A relatively new technique known as dynamic time
  warping (DTW) is an effective approach for
  automatically synchronizing batch data using key
  characteristics of a reference trajectory.
• However, as with any engineering endeavor, the
  success of the project depends greatly on the
  steps taken to apply this analytic technology.

13
The Steps the Project is Following

• Our approach at the Rouen plant will be further
  refined and followed for future applications.
  Thus, considerable thought is being given to
  project planning to achieve an installation
  success.
• The 7 project steps are
• Collection of process information
• Integration of lab and tank property data
• Instrumentation and control survey
• Historian collection
• Model development
• Training
• Evaluation of performance

14
Beta Project Execution

• Most of the time required to apply online
  analytics is associated with collecting process
  information, instrumentation and control survey,
  integration of lab data, setup of historian
  collection, and training.
• A well-planned project and the use of a
  multi-discipline team play a key role in the
  installation success.

15
Collecting Process Information

• Important that the team has a good understanding
  of process, the products produced and the
  organization of the batch control.
• Important to have a multi-discipline team
• Project meetings were conducted at the plant to
  allow operations to provide input and for the
  team to become more familiar with the process.
• This formed the basis of what we refer to as the
  Inputs Process Outputs data matrix.

16
Defining Analytic Application

• To address this application, a multi-discipline
  team was formed that includes the toolset
  provider, as well as expertise from Lubrizols
  plant operations, statistics, MIS/IT, and
  engineering staff.

Capturing project meeting discussions
Data matrix defining parameters to be considered
in the project
Beta station mapping modules
17
Beta Installation

• Beta station is layered on the existing Delta
  system using OPC.
• Mapping modules were created in the beta station
  to allow process and lab data to be collected in
  a single historian.

18
Integration of Lab Data

• Key quality parameters associated with the Rouen
  plant batch operation are obtained by lab
  analysis for grab sample. Then, a company
  typically enters the lab analysis data into its
  ERP system (SAP software in the case of
  Lubrizol)
• The properties analysis for truck shipments are
  also entered into SAP software.
• To allow this data to be used in online
  analytics, an interface was created between the
  SAP software system and the process control
  system.
• The material properties associated with truck
  shipments are used to calculate the properties of
  material drawn from storage
• It is important to characterize both the quality
  characteristics of incoming raw materials and the
  quality of end of batch characteristics.

19
Integrating Lab and Truck Shipment Data

• Lubrizol and Emerson developed applications to
  integrate lab data contained in SAP software
• Online analytic results will also be supplied to
  SAP software through this Web service interface

20
Accounting for Feed Tank Properties

• Storage material properties are calculated using
  multi-compartment tank model.
• Using the configuration of the mixing and point
  of entry parameters, the tank behavior can be
  modeled as fully mixed (CSTR), plug flow or short
  circuiting.

21
Tank Properties (Continued)

• The tank property calculations are implemented as
  a linked composite block.
• The truck or lab material properties (max. of 7
  per tank), timestamp and transfer quantity are
  wired as inputs to composite block.
• Outputs of the composite block reflect the
  calculated material properties.

22
Instrumentation and Control Survey

• A basic assumption in the application of
  analytics to a batch process is that the process
  operation is very repeatable.
• If there are issues associated with the process
  measurement or control tuning and setup, then
  these should be addressed before data is
  collected for model development.
• Parallel to the initial project meeting, an
  instrumentation and control survey was conducted
  for the two batch process areas addressed by the
  project.
• Also, changes in loop tuning were made to provide
  best process performance.

23
DeltaV Insight for Loop Tuning

• Beta station modules were created to shadow
  control loops.
• DeltaV insight was used to examine loop and get
  tuning recommendations.

24
Loop Tuning (Continued)

• Process loop dynamics and gain were automatically
  identified based on normal batch operation.
• Recommended tuning is based on the identified
  process response.

25
Historian Collection

• When the Rouen plants process control system was
  originally installed, all process measurements
  and critical operation parameters associated with
  the batch control were set up for historian
  collection in 1-minute samples using data
  compression.
• However, for analytic model development, it is
  desirable to save data in an uncompressed format.
  
• This information is collected using 10-second
  samples and saved in uncompressed format.
• This allows the data analysis to be done at a
  finer time resolution and to also further define
  a more appropriate resolution for future
  implementation.
• Analysis of the data will then define if the
  resolution needs to remain at a fine resolution
  or if it may be reduced.

26
Historian Collection (Continued)

• Emerson developed a special application as part
  of the project to create the initial data sets
  needed for model development.
• Functionality of this application is being
  incorporated into the model development tools.
  The design allows for data files to be exported
  for use in other offline applications.

DvCH data extraction utility developed to create
initial datasets for model development
27
Model Development

• The model development tools are designed to allow
  the user to easily select and organize from the
  historian a subset of the data associated with
  parameters that will be used in model development
  for a specified operation(s) and product.
• The tool provides the ability to organize and
  sequence all of the data into a predetermined
  data file structure that permits the data
  analysis.
• Once a model has been developed, it may be tested
  by using playback of data not included in model
  development.
• Since the typical batch time is measured in days,
  this playback may be done faster than real time.
  This allows the model to be quickly evaluated for
  a number of batches.

28
Interface for PCA and PLS Model Testing

• Historian data files may be played back faster
  than real time.
• Testing is done with data not used in model
  development.

29
Training

• The plant operator will primarily use the
  statistics provided by online analytics.
  Therefore, operator training is a vital part of
  commissioning this capability.
• Also, separate training classes on the use of the
  analytic tool will be conducted for plant
  engineering and maintenance.

30
Evaluation

• During the first three months of the online
  analytics, operator feedback and data collected
  on improvements in process operation will be used
  to evaluate the savings that can be attributed to
  analytics.
• It also will be used to obtain valuable input to
  improve user interfaces, displays, and the
  terminology being used in the displays.
• This will allow the project team to further
  improve the analysis modules to maximize
  operators and engineers use and understanding.

31
Business Results Achieved

• At Lubrizols Rouen, France plant online
  analytics are being applied to batch processes
  for fault detection and prediction of quality
  parameters.
• This application in the specialty chemical
  industry contains many of the batch components
  commonly found in industry.
• The analytic toolset Emerson with Lubrizol are
  collaboratively developing for this installation
  is specifically designed for batch applications
  and incorporates many of the latest technologies,
  such as dynamic time warping and hybrid
  unfolding.

32
Summary

• The use of statistical data analytics will likely
  cause people to think in entirely new ways and
  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 it will play a major
  role in ensuring 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 important impact of product
  quality parameters.
• It also will provide additional information to
  help process control engineers pinpoint where
  process control needs to be improved.

33
Where to Get More Information

• 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.htm
  l
• 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 and Randy
  Reiss
• Emerson Exchange 2008 Workshop 412 Integration
  of SAP Software into DeltaV - Philippe Moro
  Chris Worek