Improving Continuous

Process Operation Using Data Analytics

Improving Continuous

Process Operation Using Data Analytics

DeltaV Application of Data Analytics

PresentersPresenters

Frank Seibert

Eric Chen

John Caldwell

Terry Blevins

Willy Wojsznis

AgendaAgenda

A DeltaV continuous data analytics capability has been developed for on-line fault detection and quality prediction. The workshop addresses:Background on data analytics and the novel approach Emerson is taking to address continuous processes.An example of how data analytics models may be automatically developed for on-line use by the plant operator.Field trail results where for a CO2 recovery process that is part of the UT/Austin Separations Research Program.

This work demonstrates how the operation of a continuous process may be improved through the on-line use of data analytics.

Backgrond - On-line AnalyticsBackgrond - On-line Analytics

On-line Decision Support for Operations Personnel– Product quality predictions

• Predict quality problems while there is time to make on-line corrections. Not after the fact.

– Early process fault detection• Detect abnormal process operation and/or equipment problems before they affect

production

• Provide root cause analytics to direct operations or maintenance personnel to quickly correct the cause of the problem

Comprehensive monitoring to encompass both process and equipment health

Embedded technology makes on-line analytics easy to use and maintain for the average process control engineer

QUALITY CONTROL

Background - Tiered ApproachBackground - Tiered Approach

Basic Analytics – provide capability to report production KPIs, chart key quality parameters (SPC), integrate with lab systems, and report quality and production deviations.

Multivariate Analytics – Basic Analytics plus on-line fault detection and quality parameter prediction for continuous processes

Advanced Multivariate Analytics – Multivariate analytics for batch processes and continuous processes with frequent product transitions.

Background - R&D with UTBackground - R&D with UT

A research grant given to UT in 2010 to address the application of multi-state data analytics to continuous processes. Areas that this development addresses are:

Quality Prediction – Projections to Latent Structures (PLS) -Effective for correlated inputs

– Neural Network (NN) - Effective for non-linear processes

– Multiple Linear Regression (MLR) - Simple, general purpose regression Fault Detection

– Principal Components Analysis (PCA) - Powerful for recognizing patterns in data: outliers, trends, groups, relationships, etc.

• Measured/modeled disturbances – may be quantified through the application of Hotelling’s T2 statistic.

• Unmeasured/unmodeled disturbances – The Q statistic, also known as the Squared Prediction Error

Application of Multi-state Data AnalyticsApplication of Multi-state Data Analytics

Multi-state PCA and PLS may be utilized to account for changes in production rate and grade changes. Two ways of approaching the implementation of state:Standard – separate model for every state => lot of models to build and maintainEmerson’s novel approach* – one model for all state => define number of states, separate mean values and standard for the state are calculated automatically

* Patent Pending

Mixer Example - Model DevelopmentMixer Example - Model Development

The Continuous Data Analytics (CDA) Block is used to define the inputs used in fault detection and prediction of a quality parameter

User friendly names may be defined for each input.

Inputs are automatically assigned to the DeltaV historian

Mixer Example – Model Dev (Cont)Mixer Example – Model Dev (Cont)

Similar to DeltaV Neural, a Continuous Data Analytics application is used to select data for model generation.

Models are automatically developed for fault detection and quality parameter prediction.

Process delay associated with each input is automatically accounted for in model development and on-line use.

Mixer Example – Model Dev (Cont)Mixer Example – Model Dev (Cont) The results for NN, MLR and PLS for quality parameter prediction are

provided to assist the user in determining which to select for on-line use. Information on the state parameter regions and parameter mean values

are shown for each state of operation.

State Parameter

and associate

range and mean

values for each

parameter

Mixer Example – Online MonitoringMixer Example – Online Monitoring

2b. Process is in statistically abnormal condition

3a. View trend for parameter with greatest contribution

2a. View predicted quality parameter

1. Fault indicated

Field Trial - SRP CO2 Capture Pilot PlantField Trial - SRP CO2 Capture Pilot Plant

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Pilot scale absorber and stripper are used to research the recovery of CO2 gas from boiler flue gas.

– Gas Capacity, 25 m3/min

– Solvent Capacity, 130 liter/min

– Inlet CO2 Composition=1-20mol% Variations in operating conditions

directly impact the efficiency of CO2 recovery.

CO2 loading can only be measured in the lab. An on-line indication of CO2 loading would be extremely helpful in operating the pilot plant.

Field Trial – 2-stage Flash SkidField Trial – 2-stage Flash Skid

For CO2 recovery, a 2-stage flash skid is used in place of the existing stripping column with kettle reboiler.

Capital cost many be greatly reduced using this approach for CO2 recovery.

2-Stage Flash Skid2-Stage Flash Skid Skid design

allows the process to be shipped to other research facilities.

Process is well instrumented

P&ID for 2-Stage Flash SkidP&ID for 2-Stage Flash Skid

CO2 Loading Impacts Flash VesselCO2 Loading Impacts Flash Vessel Release of CO2 in the

flash vessels is influenced by the inlet concentration, operating pressure and liquid temperature.

Vessel pressure is maintained by venting the released CO2 gas and water vapor.

The CO2 and vapor flow and other measured operating conditions can be utilized to predict CO2 loading after the flash vessels

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Module Installed at UT Module Installed at UT

Two modules were installed for prediction of semi-rich amine CO2 loading and fault detection

Three modules were installed for prediction of lean amine CO2 loading and fault detection.

Selection of Training Data – Semi Rich Amine Selection of Training Data – Semi Rich Amine

Sensitivity Analysis – Semi-Rich AmineSensitivity Analysis – Semi-Rich Amine

Training ModelTraining Model

Select State

Parameter

Selection of Model – PCA/PLS/NNSelection of Model – PCA/PLS/NN

On-line Operator Interface On-line Operator Interface

Fault Detection View – Normal OperationFault Detection View – Normal Operation

Fault Detection - StartupFault Detection - Startup

Examining Contribution Examining Contribution

Installation StatusInstallation Status

Preliminary PCA models have been developed for semi-rich and lean amine CO2 loading using process data at UT.

Not enough lab data was available by Emerson Exchange to create PLS models for prediction of CO2 loading – process startup delayed by heat exchange gasket and pump seal failure.

Simulation of 2-Stage Flash SkidSimulation of 2-Stage Flash Skid

A dynamic simulation of the 2-stage flash skid has been developed to test and demonstrate continuous data analytics – see this in Advanced Control booth in Exhibit Area.

Insight gained through use of this simulation will be helpful in implementation of data analytics on the actual process.

Dynamic Process Simulation ResponseDynamic Process Simulation Response Process reaches steady state conditions after approximately

30 minutes – closely reflecting actual process response

Introduction of Process FaultIntroduction of Process Fault

Fault may be introduced

Model Verification Using Simulator DataModel Verification Using Simulator Data

Feed Temperature Fault IntroducedFeed Temperature Fault Introduced

Examine ContributionExamine Contribution

Examine Impact on CO2 LoadingExamine Impact on CO2 Loading

Business Results AchievedBusiness Results Achieved

An easy to use on-line prediction of quality parameters and fault detection have been developed for continuous processes and demonstrated in a field trail.

Plant operator may use the continuous data analytics quality parameter prediction to compensate for process changes sooner than is possible using lab data.

Early process fault detection and identification of parameters associated with a fault will allow plant maintenance to more quickly resolve fault conditions and thus should lead to overall improvement in plant operation and efficiency.

SummarySummary

Data analytics for continuous process quality parameter prediction an fault detection are being applied and demonstrated on a 2-state flash skid used for CO2 recovery.

The operator will use this on-line data analytics to more quickly respond to conditions and faults that impact process operation and quality parameters.

Where To Get More InformationWhere To Get More Information

More information on the 2-stage flash skid operation will be presented in the workshop “8-2241 Commissioning Highly Interactive Process”.

Examples of process simulation using DetlaV modules is contained in Chapter 15 of “Control Loop Foundation – Batch and Continuous Processes” - http://www.controlloopfoundation.com/about-the-book.aspx

See DeltaV product updates by John Caldwell and Dawn Marruchella for information on target release dates for batch and continuous data analytics.