1

Application of Online Data

Analytics to a Continuous Process

Polybutene Unit

Regina Stone Process Improvement Engineer

Robert Wojewodka Technology Manager and Statistician

Efren Hernandez Process Control Superintendent

Terry Blevins Principal Technologist

2

Presenters

Regina Stone

Robert Wojewodka

Efren Hernandez

Terry Blevins

3

Introduction

Just as with batch processing, data analytics

can be applied to continuous processes for on-

line prediction of quality parameters and

detection of fault conditions.

In this workshop we present:

Background and example of continuous data

analytics.

Field trial of continuous data analytics at

Lubrizol, Deer Park, TX on a polybutene unit

and refrigeration system.

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The Lubrizol Corporation Segments

The Right Mix of People, Ideas and Market Knowledge

• Advanced chemical technology for global transportation, industrial

and consumer markets

• Unique, hard-to-duplicate formulations resulting in successful

solutions for our customers

• A talented and committed global work force delivering growth

through skill, knowledge and imagination

Lubrizol Additives Lubrizol Advanced Materials

Growth. Innovation. People.

5

Lubrizol Leading Market Positions

6

Emerson Emerson

DeltaV modules knowledge

Provide Lubrizol with a field trial

tool for online quality parameter

prediction and fault detection

Provide technical support for

difficulties experienced while

using software

Use Lubrizol’s feedback to

further develop the Continuous

Data Analytics software package

Lubrizol Lubrizol

Process and analysis knowledge

Apply software to a continuous

process and identify

measurements

Build models and evaluate and

validate the modeling software

Implement models into ongoing

unit operations

Collect feedback and report

findings to Emerson

Emerson and Lubrizol Roles

Key Goal:

Collaborate with Emerson to develop and test the

Continuous Data Analytics software package.

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Operators work in a highly complex, highly correlated and

dynamic environment each day.

Any advanced warning of deviations is valuable.

Operators manage a large amount of data and information

on a continuously operating unit. Even with automation,

only so much can be monitored and managed at one time.

Any help with continuous monitoring across many variables is

valuable.

The goal is to prevent the undesirable effects of an

abnormal situation by early detection of precursor

deviations and predict product quality real-time.

The Setting

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Background on Analytic Techniques

Analytic tools can be applied to both continuous

and batch processes.

Application to continuous processes require

special consideration such as:

– Varying flow rates

– Product grade transitions

For model development and on-line use it is

necessary to allow real-time access to

measurements and lab data associated with

product quality and feedstock.

9

General Concepts – A Process

Generic continuous process flow diagram.

INPUTS

PROCESS

OUTPUTS

Very much like batch processing, continuous

process applications can be simplified down to

these major blocks of activity

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General Concepts – A Process Initial Conditions

Feed Stock Analysis

Measurements reflecting operating

conditions that impact product quality

(X Parameters, In-Process Y Parameters)

Lab Analysis of

Product Quality

(Y Parameter)

Generic continuous process flow diagram.

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Basic Concepts

SPC Chart for Variable 1

0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907

UCL = 96.5239

LCL = 83.6576

SPC Chart for Variable 1

0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907

UCL = 96.5239

LCL = 83.6576

Univariate SPC Charts

…. Time ….

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Basic Concepts

SPC Chart for Variable 1

0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907

UCL = 96.5239

LCL = 83.6576

SPC Chart for Variable 2

0 10 20 30 40 50 60

Observation

0

2

4

6

8

10

12

X

CTR = 5.9426

UCL = 11.5478

LCL = 0.3374

Anything atypical

with this point? Anything atypical

with this point?

Univariate SPC Charts

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SPC Chart for Variable 1

0 10 20 30 40 50 60

Observation

83

86

89

92

95

98

X

CTR = 90.0907

UCL = 96.5239

LCL = 83.6576

SP

C C

ha

rt fo

r V

aria

ble

2

010

20

30

40

50

60

Ob

se

rva

tio

n

02468

10

12

XC

TR

= 5

.9426

UC

L =

11

.5478

LC

L =

0.3

374

Control Ellipse

82 86 90 94 98

Variable 1

-1

2

5

8

11

14

Va

ria

ble

2

Variable 1

Variable

2

Basic Concepts

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Multivariate SPC Chart Multivariate SPC Chart Multivariate Control Chart

UCL = 10.77

0 10 20 30 40 50 60

Observation

0

4

8

12

16

20

24

T-S

quare

dMultivariate Control Chart

UCL = 10.77

0 10 20 30 40 50 60

Observation

0

4

8

12

16

20

24

T-S

quare

d

Basic Concepts

…. Time ….

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Process M1

M2

M3

M4

M5

M6

M7

M8

M9

....

Time Delays

Q1

Q2

Q3

... Online

Measurements

Quality

Parameters from

Lab

In a continuous process there can be a significant differences in the

time required for each on-line measurement to impact processing or

a measured quality parameter.

X - space Y - space

The Nature of Continuous Data

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The Nature of Continuous Data

The normal operating point of process measurement

may change with process throughput. The

parameter(s) that drive change in the process are

known as state parameters (e.g. production rate). In

this example, the state parameter is the fuel demand.

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The Nature of Continuous Data

Product grade can also be the state parameter in some

cases. A change in the product grade being made is a

change in the state parameter.

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Through the use of Principal Component Analysis

(PCA) it is possible to detect abnormal operations

resulting from both measured and unmeasured faults.

– Measured disturbances – may be quantified through the

application of Hotelling’s T2 statistic.

• The T2 plot characterizes the amount of process variation that can be

explained by the model and how it compares to “typical” operation.

– Unmeasured disturbances – The Q statistic, also known as the

Squared Prediction Error (SPE) or DMODX, may be used.

• The Q plot characterizes the amount of process variation that cannot

be explained by the model.

Projection to latent structures, also known as partial

least squares (PLS) is used to provide operators with

continuous prediction of quality parameters.

Online Data Analytics

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Preparation for the On-line Trial

Form a multi-discipline team that includes plant operations

Capture team input using

an “input-process-output”

data matrix

Enter lab data

Collect lab data on quality parameters and feedstock

Survey Instrumentation, tune control loops

Conduct formal operator training

This is the same approach that we took for our batch analytics trial several years ago.

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Creating a Data Analytics Model The following steps are required to develop and deploy a data analytics model:

• the process overview and identify the input, process, and output measurements

• the process overview and identify the input, process, and output measurements

Define

• a module that contains a Continuous Data Analytics block and configure for measurements that may impact quality

• a module that contains a Continuous Data Analytics block and configure for measurements that may impact quality Create

• the module that contains the CDA block and the continuous data historian and begin entering lab data

• the module that contains the CDA block and the continuous data historian and begin entering lab data Download

• process data over the full dynamic operating range • process data over the full dynamic operating range Collect

• the selected historian data in the CDA application and perform a sensitivity analysis

• the selected historian data in the CDA application and perform a sensitivity analysis Analyze

• a model by selecting the state parameter and method. Validate the model for prediction accuracy using data then download the module

• a model by selecting the state parameter and method. Validate the model for prediction accuracy using data then download the module Generate

• the web browser to view on-line fault detection and quality parameter prediction; revalidate further once on-line

• the web browser to view on-line fault detection and quality parameter prediction; revalidate further once on-line

Launch

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Define Scope of Lubrizol Field Trial

A prototype of a future

DeltaV capability for

continuous process

quality parameter

prediction and fault

detection is being tested

in a field trial at Lubrizol

on:

Polybutene Unit

– Viscosity

Refrigeration System

– Dynamic

Compressor

Efficiency

Refrigeration System

Dynamic

Compressor

Efficiency

Operation 1 Reaction Operation 3 Operation 4

AT

Product

Bulk

Viscosity

Polybutene Unit

2+ Hours

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Create & Configure Module

1. Create module in DeltaV

Control Studio

2. Configure CDA

block for

measurement

inputs that reflect

conditions that

impact quality

3. Download module and

verify on-line that module

is collecting and calculating

data

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Collect Process Data

Wait for process data to be collected by the historian

that reflect the normal process changes over the full

dynamic operating range.

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Analyze Historian Data

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Generate Model

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Validate Model

Model is fairly good for the

higher grade of polymer,

but not as good for the

lower grade of polymer.

Shared this information

with Emerson, who then

made some code

changes in the

software.

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Validate Model Again

With the code changes,

carefully excluding

outliers, and the data time

delay estimate enhanced,

the model has been

greatly improved.

Model will be launched for

the online trial after

maintenance turnaround.

Additional model

development is ongoing.

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Dynamic Compressor Efficiency

An on-line calculation of

dynamic compressor

efficiency of both

compressors was

implemented in DeltaV.

PLS/PCA model for

efficiency prediction and

fault detection were

trained using the on-line

calculation of efficiency.

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Compressor Efficiency Model

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Model Verification – Compressor Efficiency

Excellent Fit

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Process Analytics Overview

In the Continuous

Data Analytics

Overview screen,

Fault detection

status and quality

parameter prediction

for deployed

PCA/PLS model(s)

are displayed.

A web browser can

be used to access

this overview if a

station has Ethernet

access to the field

trial station.

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Quality Parameter Prediction

The impact of process variation on the quality parameters can be seen by

selecting the quality parameter tab to view the predicted quality parameter.

Predicted values over time can be obtained by clicking in the trend area.

Under normal operating conditions, the predicted value should fall within the

product specification range (green band).

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Fault Detection

By clicking on a

monitored process

from the overview

and selecting the

fault detection tab,

the calculated

statistics are shown

as Indicator 1 (T2)

and Indicator 2 (Q).

A fault is indicated if

either statistic

exceeds an upper

fault detection limit

of 1.0.

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Two Step Monitoring Procedure

– If either Fault Detection plot exceeds or approaches the upper fault detection limit of 1.0, click on that point in the trend and

• Select the parameter(s) in the left pane of the screen that contributed to the fault

• Evaluate the parameter trends from process operation standpoint

• Take corrective action if necessary.

– Inspect impact of the fault on quality prediction plot to find out how quality could be affected.

If a fault is indicted in the analytics overview screen, then select the associated process and the Fault Detection Tab.

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Good Start but More is Needed Improve similarity between the Emerson on-line batch and

continuous analytics offerings

Improve process analysis diagnostics

Support additional variables in the analysis

Support “vector data” types (e.g. IR, GC, MS)

Include Discriminant analysis in addition to PLS in both the batch

and continuous offerings

Incorporate on-line monitoring of the model’s health

Implement adaptive updating of a model after initial deployment

Create the ability to handle select relevant variables when multiple

processing paths may be utilized

Improve ability to make data available for additional analysis and

model validation outside of DeltaV

Streamline network access to the online web interface

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Installation and Network Setup

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Summary

Lubrizol, Deer Park TX is testing a future DeltaV capability for

quality parameter prediction and fault detection for

continuous processes

Initial assessments indicate that the methodology will be

applicable to continuous processes for:

– Process monitoring and fault detection

– On-line prediction of product quality

– Application to “non traditional” settings such as equipment efficiency

Good starting point but more needs to be developed to have

these modules applicable for use

We encourage Emerson to continue their development in this

area to further develop the on-line continuous analytics

module(s)

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Data Analytics Workshops

Learn more about continuous and batch data analytics

by attending the following workshops at this year’s

Emerson Exchange:

8-1322 Application of Online Data Analytics to a Continuous

Process Polybutene Unit

8-2092 – Practical considerations for installing and using Batch

Analytics

8-1965 Batch Analytics Applied to a Large Scale Nutrient Media

Preparation Process

MTE-4021 Advanced Control Foundation – Tools & Techniques

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Where To Get More Information

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate PLS for

Continuous Process Monitoring, ACC, March, 2012

J.V. Kresta, J.F. MacGregor, and T.E. Marlin., Multivariate Statistical

Monitoring of Process Operating Performance. Can. J. Chem.Eng.

1991; 69:35-47

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate Analytics for

Continuous Processes, Journal of Process Control, 2012

MacGregor J.F., Kourti T., Statistical process control of multivariate

processes. Control Engineering Practice 1995; 3:403-414

Kourti, T. Application of latent variable methods to process control and

multivariate statistical process control in industry. International Journal

of Adaptive Control and Signal Processing 2005; 19:213-246

Kourti T, MacGregor J.F. Multivariate SPC methods for process and

product monitoring, Journal of Quality Technology 1996; 28: 409-428