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Application of On-line Data Analytics to a Continuous Process Polybutene Unit

An In-Depth Discussion on Model Building

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Presenters

QiWei Li

Efren Hernandez

Robert Wojewodka

Terry Blevins

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Introduction

Continuous data analytics provides online prediction

of quality parameters and detection of fault conditions

Topics

– Introduction of continuous data analytics (CDA)

– Model building techniques and examples

– On-line view platform implementation

– Benefits and achievements

– Q&A

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The Lubrizol Corporation Segments

• 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

The Right Mix of People, Ideas and Market Knowledge

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Lubrizol Leading Market Positions

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Lubrizol

Evaluate the performance of quality

parameter predictions using data

analytics in comparison to the on-line

analyzers in a continuous

polymerization process unit.

Develop simple and informative user

platform for continuous performance

monitoring and data analysis

Evaluate process quality control

schemes based on parameter

predictions using data analytics

Emerson

Support ALL Lubrizol objectives

Test CDA prototype functionality in

collecting data, developing models and

on-line operation

Developing recommendations for

enhancing CDA prototype and future

CDA product

Providing Lubrizol field trial feedback

on CDA for DeltaV product planning

group

Emerson and Lubrizol Objectives

Key Goal:

Collaborate to develop and improve the Continuous

Data Analytics software package.

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General Concepts – A Process

INPUTS

PROCESS

OUTPUTS

Very much like batch processing, continuous

process applications can be simplified down to

these major blocks of activity

Generic continuous process flow diagram.

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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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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?

General Concepts – Univariate SPC Charts

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Statistic – Multi-Variant Analysis

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

X

CT

R =

5.9

426

UC

L =

11.5

478

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

Varia

ble

2

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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

quar

ed

Basic Concepts

…. Time ….

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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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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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Model Building

• 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 Create

• 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 Collect

• the selected historian data in the CDA application, clean up the data, 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 Generate

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

Launch

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Model Building

Learning from Data – Define the key operating regime parameter in the model as the “state

parameter”

– All variables that impact the quality parameter must be captured

– All variations of the typical operating conditions must be captured

Quality Parameter

State Parameter

Variable X

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Lubrizol Field Trial Scope

Operation 1 Reaction Operation 3 Operation 4

A

Product

Bulk

Viscosity

Polybutene Unit

2+ Hours

Refrigeration System

Dynamic

Compressor

Efficiency

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Refrigeration Model

Quality parameter: Compressor Efficiency

– Maximize compression efficiency

– Detect fault processing conditions

12 out of 22 process inputs incorporated into model

No process delay 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = 100 ∗ℎ 𝑝2, 𝑠1 − ℎ 𝑝1, 𝑡1ℎ 𝑝2, 𝑡2 − ℎ 𝑝1, 𝑡1

Where,

h(p2,s1 )=isentropic enthalpy for suction entropy

s1 and discharge pressure p2

h(p2,t2 )=enthalpy at discharge pressure p2 and

temperature t2

h(p1,t1 )=enthalpy at suction pressure p1 and

temperature t1

s1=entropy at suction pressure p1 and

temperature t1

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Below models cross verifies predicted (green) against actual (blue)

efficiency

Variation in operating condition lowered model effectiveness

Model Improvement – Example 1

r2=0.97 r2=0.60

2012 Model 2013 Model

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Fault in model contributed by change in feed composition

Re-trained model to reflect feed composition variation in 2013

Model Improvement – Example 1

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The adjacent compressor’s activity had a direct impact on

existing model

Re-train not recommended due to higher frequency of changes

Model Improvement – Example 2

1 Active Compressor

r2=0.05

2 Active Compressor

r2=0.92

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Model Improvement – Example 2

Defined the number of compressors active as “state parameter”

Collect data to cover all process condition variations

Re-train model

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Refrigeration Model Exhibition

r2=0.97

2013 Compressor Efficiency Model

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Combined Viscosity Model

Quality parameter: Finished product viscometer output

– Distinguish high and low viscosity cuts

– Detect abnormal shift in viscosity

– Detect faulty processing conditions

11 out of 28 process inputs incorporated into model

2+ hour process time delay

Low Vis Input

High Vis Input

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Model Improvement – Example 3

Quality parameter (green trend) failed to trigger the selector logic

because the low range viscosity meter’s top limit in the field was lowered

to a value below the selector trigger point

Issue resolved after modifying logic statements

Intimate process knowledge are great supplements for model integrity

upkeep

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Model Improvement – Sensitivity Analysis

Multiple Variables

– Multiple dimensions of data analysis (Time range, time delay, # of

principle components, etc.)

– One variable is changed at a time to define the optimized model

X Hour Delay Y Hour Delay

r2=0.79 r2=0.94

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Combined Viscosity Model Exhibition

2013 Combined Viscometer Model (Cross Verification)

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Lab Viscosity Model

Quality parameter: Finished product lab analyzed viscosity

– Verify viscometer’s accuracy

20 out of 28 process inputs incorporated into model

2-3 hour process time delay

Samples are taken every 2 hours (non-continuous)

Jan Mar

Jan 6

420/636 sample points used in model

Mar 1

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Lab Viscosity Model – Self Verification

Models are good fit for self verification with existing data

However, next slide’s cross-verification shows weaker

extrapolation compared to previously displayed models

2012 Model 2013 Model

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Lab Viscosity Model

Compared to the compressor and combined vis models, the

2013 lab viscosity model requires much more work ahead

Possible causes for deviation includes fewer sample points,

additional lab sample delay, missing input variables, etc.

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Pureweb On-line View

Pureweb on-line view web interface developed by Emerson

– Web interface deployed in process unit for operator to use

– Quality prediction, fault detection, and deviation alarm are featured

Operator Feedbacks:

– “Our plant is getting younger as more experienced operators retire and new hires come in. The prediction tool will give new operators a good idea of what they’re making in the reactor and reduce the chances of making off spec material.”

– “Poly Plant is a complex system. With all the temperature, pressure, level, and other variables, it is sometimes overwhelming to run the board. Data analytics will help us focus on the more important factors that drive the reaction and allow us to make an informed decision before making a system change.”

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On-Line View – Quality Prediction

• Prediction is expected to fall within the

confidence bands

• Auto-scaling zooms in to increase

resolution

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On-Line View – Fault Detection

• Fault is significant if either indicator exceeds a value of 1.0

• Clicking the left field shows deviations for individual variables

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Application Station

(existing)

Level 2

Level 2,5

Level 3

Level 4

PI

(existing) A Plant

Corporate

PI LAB DATA

(Matrikon/opc Tunneller)

Web Server

Access

Measurements

(Matrikon/opcTunneller)

Remote Desktop

Access - Citrix

Beta Team – Model Building using remote desktop,

On-line monitoring using Web Browser/ remote desktop. A Plant Management - On-line

monitoring using Web Browser

(existing) (existing)

DMZ 2.5

DMZ 2

Production Control Network

Continuous Data Analytics

IIS Server

PureWeb

(existing)

Poly Control Room - On-line

monitoring using Web

Browser

Layers of Network Architecture

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Product Integrity and Productivity Improvement

– Quality prediction promotes product integrity

– Fault detection improves mechanical reliability and prevent

production time lost

Personnel & Process Safety

– Fault detection can recognize abnormal system behaviors and act

as secondary safety safeguards in addition to alarms

Business Results Achieved

Production Detects

Deviation Respond to Deviation

Avoid Lost Production

Time

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Business Results Achieved

Operation Efficiency

– A quick glance informs the operators on the state of the process

– Operators can utilize future viscosity predictions to reduce transition

time in between product grades

Training

– The web interface is an easy-to-learn training module for less

experienced operators

– CDA provides experienced operator with another layer knowledge

on the intermittent relationship of over 10 variables in a continuous

process

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Summary

Continuous Data Analytics has successfully provided

Lubrizol, Deer Park TX with quality prediction and fault

detection capabilities

With the combined effort of Lubrizol and Emerson

representatives, four analytical models were developed for

the compressors and the production unit

These four models were deployed into unit operation

CDA model’s quality prediction and fault detection features

received positive feedback from operations

Extensive knowledge on CDA and model building is gained

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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:

8-4775 Challenges and Solutions in Data Analytics Application for a Distillation Column

8-4342 How to install Batch Analytics on a non-V12 DeltaV system

8-4240 Application of On-line Data Analytics to a Continuous Process Polybutene Unit

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Where To Get More Information

Regina, Sansoni, T., Blevins, Application of Online Data Analytics to a Continuous Process Polybutene

Unit, October, 2012

Terrence Blevins, Willy K. Wojsznis and Mark Nixon Advanced Control Foundation – Tools, Techniques,

and Applications, ISA, 2013

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

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Thank You for Attending!

Enjoy the rest of the conference.