Coupling Process Control Coupling Process Control Systems and Process AnalyticsSystems and Process AnalyticsRobert Wojewodka –Technology Manager

Philippe Moro –IS Manager 

Terry Blevins – Principal Technologist

PresentersPresentersPresentersPresenters

• Robert Wojewodka

• Philippe Moro

• Terry Blevins

IntroductionIntroductionIntroductionIntroduction

• The Lubrizol Corporation and Emerson are partnering to explore the technical challenges of applying online analytics in a batch operation.

• In this session, we will present:– The role Principal Component Analysis (PCA) and Projection to

Latent Structures (PLS) can play in fault detection and prediction of end-point quality parameters.

– Prototype tools that Emerson developed for this study

– The approach used in testing on-line analytics on a batch process at a Lubrizol plant.

– Continuing collaboration to address on-line data analytics with DeltaV.

PAT FrameworkPAT FrameworkPAT FrameworkPAT Framework

The PAT framework defines the following tool categories:• Multivariate data acquisition and analysis tools• Modern process analyzers and process analytical

chemistry tools• Process and endpoint monitoring and control tools• Continuous improvement and knowledge management

tools.

An appropriate combination of some, or all, of these tools may be applicable to a single unit operation or to an entire manufacturing process and its quality assurance.

Three aspects of data analyticsThree aspects of data analyticsThree aspects of data analyticsThree aspects of data analytics

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

Process AnalyticsProcess AnalyticsProcess AnalyticsProcess Analytics

• 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 on-line application of Analytics for prediction and fault detection and identification in batch operations.

• Beta installation to demonstrate this technology is targeted to be on-line in mid-2007 timeframe.

• The research grant given to UT is funding the work of a PhD graduate student, Yang Zhang, under the supervision of Professor Tom Edgar.

Summary of ResearchSummary of ResearchSummary of ResearchSummary of Research Much of this

funded research is summarized in chapter 8 of the book “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”

Detection of Abnormal Detection of Abnormal Operation Operation Detection of Abnormal Detection of Abnormal Operation Operation

• Measured Disturbances – The Score space associated with Principal Component Analysis, PCA, captures contributions that can be associated with process measurements. Deviations in the principal component subspace score may be quantified through the application of Hotelling’s T2 statistic.

• Unmeasured Disturbances – The Residual space that is not captured by the score space reflects changes in unmeasured disturbances that impact the operation. The Q statistic, Squared Prediction Error (SPE), is a measure of deviations in process operation that are captured by the residual subspace.

• Identification of the primary measurements that contribute to a process deviation will be done using contribution plots.

Quality Parameter EstimationQuality Parameter EstimationQuality Parameter EstimationQuality Parameter Estimation• The detection of deviations in quality parameters will

be addressed through the use of Projection to Latent Structures, PLS. Through this technique, it is possible to maximize the covariance (between the predictor (independent) variables X and the predicted (dependent) Y parameters.

• Where the objective is to classify the operation results, then discriminate PLS, PLS-DA, will be applied.

• The fault detection, identification, techniques that may be used with PCA can be applied in exactly the same way for PLS e.g. Q and T2 statistics, contribution plots.

Planned On-line Analytic SupportPlanned On-line Analytic SupportPlanned On-line Analytic SupportPlanned On-line Analytic Support

Three function blocks will be developed for beta testing of on-line process analytics

– PCA Block

– PLS Block

– Analyzer Block

Each block supports the models identified for the associated process unit.

Example – PCA Block FunctionExample – PCA Block FunctionExample – PCA Block FunctionExample – PCA Block Function

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

• The wide range of operating conditions presents challenge in the design and commissioning.

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

Reagent e.g. Ammonia

FC 202FC 202

FT 202

Air

pH

AC 205AC 205

DissolvedOxygen

Vent

PT 208

PC 208PC 208

RSPCharge e.g. Media

FT 203 Coolant

supply

IT 209

LT 210

To Harvest

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

Models Vary with Product/phase and Models Vary with Product/phase and UnitUnitModels Vary with Product/phase and Models Vary with Product/phase and UnitUnit

Batch AnalyticsBatch AnalyticsBatch AnalyticsBatch Analytics

• To support the development of PCA/PLS/PLS-DA models for the units making up a batch process, the following information must be collected by the control system historian:

• Product Identifier• Operation phase/state of the unit• Available process measurements for the unit• Identifier of shared resources• Lab analysis associated with unit inputs or outputs.• Historic data targeted for model development should be

saved as periodic samples with data compression turned off.

Added Support Is Required for Lab Added Support Is Required for Lab ResultsResultsAdded Support Is Required for Lab Added Support Is Required for Lab ResultsResults

ModuleLab Results

Analytic Block

Controller

DeltaV Historian

Operator Station

Use of Lab Results in DeltaV

ProPlus Off-line

Modeling

Model Development - Processing DataModel Development - Processing DataModel Development - Processing DataModel Development - Processing Data

• Analysis is based on one minute average samples – minimizing impact of noise.

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.

Model Testing/VerificationModel Testing/VerificationModel Testing/VerificationModel Testing/Verification

• High fidelity, dynamic simulation models of the target process will utilized in the beta test to support PCA/PLS development and testing

• For off-line testing, the beta station will act as a Virtual Plant in which the mathematical simulation of the process runs with identical control loops and the same tuning parameters as an actual plant.

Virtual Plant Used For Initial Virtual Plant Used For Initial Checkout Checkout Virtual Plant Used For Initial Virtual Plant Used For Initial Checkout Checkout

Operator Interface – Fault DetectionOperator Interface – Fault DetectionOperator Interface – Fault DetectionOperator Interface – Fault Detection

• Plots of the T2 and Q statistics will be provided in this interface.

• Using the slew button, it will be possible to view the operation of previous batches processed by this unit.

• By clicking in the trend area associated with the current batch, the operator may view a score contribution plot to determine the parameter(s) that caused the deviation

Operator InterfaceOperator InterfaceOperator InterfaceOperator Interface

• By clicking in the trend area associated with the T2 and Q plots for current batch, the operator may view a score contribution plot to determine the parameter(s) that caused the deviation

Prediction of Key Quality ParametersPrediction of Key Quality ParametersPrediction of Key Quality ParametersPrediction of Key Quality Parameters

• When the dynamo for the PLS/PLS-DA function block is reference by the operator, then the following view will be provided to examine the predicted quality parameter.

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. multiway-PCA/PLS.

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

Beta InstallationBeta InstallationBeta InstallationBeta Installation

SummarySummarySummarySummary

• On-line process analytics can play in batch processes through fault detection and projection of quality parameters.

• Plans are in place to do beta testing of on-line analytics at a Lubrizol plant

• Results of the beta will be presented at Emerson Exchange, 2008.

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

• “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”