Dofasco’s Enhanced Monitoring System Vit Vaculik and Adam Smyth OSI PI Users Conference San Francisco May 13, 2003 Vit Vaculik and Adam Smyth OSI PI Users

Dofasco’s Enhanced

Monitoring System

Vit Vaculik and Adam SmythOSI PI Users Conference

San FranciscoMay 13, 2003

Vit Vaculik and Adam SmythOSI PI Users Conference

San FranciscoMay 13, 2003

© Dofasco Inc., 2003. All rights reserved. Dofasco Inc. makes no representation or warranty as to the contents of this presentation and shall not be liable to any person in connection with its use.

• Introduction

• Caster SOSTM and New Requirements

• System Enhancements

• Visualization

• Expanded Monitoring

• Historical Support Utilities

• Summary

• Introduction



• Visualization



• Summary

AgendaAgenda

• Introduction



• Visualization



• Summary

• Introduction



• Visualization



• Summary

AgendaAgenda

Dofasco Hamilton Works

Dofasco Hamilton Works• One of Canada’s largest

fully integrated steel producers

4.8 million tons of steel shipped in 2002 with sales $3.6 billion (Cdn)

7500 employees

1000 in technology areas

• A North American leader in profitability

• One of Canada’s largest fully integrated steel producers

4.8 million tons of steel shipped in 2002 with sales $3.6 billion (Cdn)

7500 employees

1000 in technology areas

• A North American leader in profitability

The ProcessThe Process

LadleLadle

TundishTundish

BendingBending

UnbendingUnbending

RunoutRunout

Caster SOSTMCaster SOSTM

Process

OpportunitiesCaster Stability/Quality Prediction/Breakout Avoidance

DataTemperatures

Cooling Water Data

Stopper Rod Position

Heat Transfer

Negative Strip Time

Etc.

Alarms and Warnings through 2 Stability Indices

Top 5 Influences to the Problem

Trending of Data

Thermal Contour Map

InformationAutomatically Slow Cast Speed

Automatically Disposition Product

Strand Operator Slows Down Cast Speed

Operator Deselects Faulty Signal from the Model

Action

DAT SolutionCaster SOS

Caster SOSTM Concept Caster SOSTM Concept

CASTER SOS

ALARMALARM

ALARM

ALARM

ALARM

Erratic MouldLevel

SEN ChangeRequired

Sensor Failure

Breakouts

Other Events

Good Operation

New RequirementsNew Requirements• Enhanced Visualization of data

• Real-Time display with PI data

• Expand Monitoring System to include Transient Casting Conditions• PI as an integral component

• Enhanced Supporting Analysis Utilities• Analyzes large PI archive data set

• Enhanced Visualization of data• Real-Time display with PI data

• Expand Monitoring System to include Transient Casting Conditions• PI as an integral component

• Enhanced Supporting Analysis Utilities• Analyzes large PI archive data set

• Introduction



• Visualization



• Summary

• Introduction



• Visualization



• Summary

AgendaAgenda

Thermal MapThermal Map


• Purpose: provide the operators with an enhanced view of the thermocouple data so that it can be used to help diagnose Caster SOSTM alarms

• Purpose: provide the operators with an enhanced view of the thermocouple data so that it can be used to help diagnose Caster SOSTM alarms


ConfigDatabase

Plant Network

PLC & DCS

PI Server PI Server

PI PumpClass

Active X

PI API

EVM Arch

Thermal MAP

OSI PI Servers

Resamp toDistance

Charts




Benefits

• Temporal and spatial visualization of the data

• Operator able to see hot and cold spots in the mould

• Operator able to diagnose Caster SOSTM alarms based on thermal map display

Benefits

• Temporal and spatial visualization of the data

• Operator able to see hot and cold spots in the mould

• Operator able to diagnose Caster SOSTM alarms based on thermal map display

Case Study #1Case Study #1

• Caster SOSTM and Thermal Map identified formation of a sticker in the mould

• Thermal Map not commissioned for the event

• Sticker Breakout Occurred

• Caster SOSTM and Thermal Map identified formation of a sticker in the mould

• Thermal Map not commissioned for the event

• Sticker Breakout Occurred

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

Example 1Example 1

• Introduction



• Visualization



• Summary

• Introduction



• Visualization



• Summary

AgendaAgenda

Transient Caster MonitoringTransient Caster Monitoring• Start cast

• SEN change

• Tundish fly

• Plate insert

• Start cast

• SEN change

• Tundish fly

• Plate insert

Transient Caster MonitoringTransient Caster Monitoring

Steady State Casting

• 25% of breakouts found to be during start cast operation

• 25% of breakouts found to be during start cast operation

Start Cast MonitoringStart Cast Monitoring

• Purpose: provide the operator with on-line monitoring of the start cast operation giving them a continuous indication of start cast stability

• Purpose: provide the operator with on-line monitoring of the start cast operation giving them a continuous indication of start cast stability

• Inherent characteristics in process data of batch or start-up processes

• Dynamic (non-stationary)

• Auto-correlated

• In a finite duration

• Inherent characteristics in process data of batch or start-up processes

• Dynamic (non-stationary)

• Auto-correlated

• In a finite duration

Nature of Batch/Start-up ProcessesNature of Batch/Start-up Processes

Continuous process Batch/Start-up process

0 500 1000 150060

70

80

90

100

110

120

130

140

150

Observations

Tem

pera

ture

(D

egre

es)

0 500 1000 150060

70

80

90

100

110

120

130

140

150

Observations

Tem

pera

ture

(D

egre

es)

0 1000 2000 3000 4000 5000 6000 7000 8000 9000105

110

115

120

125

130

135

140

145

Observations

Tem

pera

ture

(D

egre

es)

0 1000 2000 3000 4000 5000 6000 7000 8000 9000105

110

115

120

125

130

135

140

145

Observations

Tem

pera

ture

(D

egre

es)


Monitoring Module

Process StateDetermination

Run-timeOperationMonitoring

Start-upMonitoring

Model Set

MPCAModels

Standard PCAModels

.

.

.

Data AcquisitionModule

ProcessHistoricalDatabase

ModelDevelopment

Module

ContinuousCaster

Process

Sensors

Operators and HMI Computer Monitoring Module



Start-upMonitoring

Model Set

MPCAModels

Standard PCAModels

.

.

.



ModelDevelopment

Module

ContinuousCaster

Process

Sensors

Operators and HMI Computer

Monitoring Module



Start-upMonitoring

Model Set

MPCAModels

Standard PCAModels

.

.

.



ModelDevelopment

Module

ContinuousCaster

Process

Sensors

Operators and HMI Computer Monitoring Module



Start-upMonitoring

Model Set

MPCAModels

Standard PCAModels

.

.

.



ModelDevelopment

Module

ContinuousCaster

Process

Sensors

Operators and HMI Computer


Cast Speed

State

Steady

Start-up

Idle

Pre-Start-up

Case Study #2Case Study #2

• System in background testing

• System alarms at the end of start cast

• Mould thermocouples abnormally inverted at end of start cast (abnormal)

• Could have lead to start cast breakout

• System in background testing

• System alarms at the end of start cast

• Mould thermocouples abnormally inverted at end of start cast (abnormal)

• Could have lead to start cast breakout

Thermocouples begin to invert

Alarming

State switches from start-cast to run-time

• Introduction



• Visualization



• Summary

• Introduction



• Visualization



• Summary

AgendaAgenda

Performance Monitor of Process AlarmsPerformance Monitor of Process Alarms

• Purpose: Analysis tool that can be used to statistically analyze PI data

• Purpose: Analysis tool that can be used to statistically analyze PI data

Performance Monitor of Process AlarmsPerformance Monitor of Process AlarmsFeatures• User selects PI tags, start/end time/interval

for the investigation

• Advanced condition based data retrieving and analysis (complex queries can be setup with and/or logic operators)

• Develops generic report

• Analysis can be scheduled to run periodically

Features• User selects PI tags, start/end time/interval

for the investigation

• Advanced condition based data retrieving and analysis (complex queries can be setup with and/or logic operators)

• Develops generic report

• Analysis can be scheduled to run periodically


Example 3Example 3

Example 3Example 3




Performance Monitor of Process AlarmsPerformance Monitor of Process AlarmsBenefits

• Other tools (E.g. Data link for Excel) limited by the amount of data that can be analyzed at once

• Enables what/if scenarios to be performed in seconds

• Support the monitoring system

• determine alarming thresholds

Benefits

• Other tools (E.g. Data link for Excel) limited by the amount of data that can be analyzed at once

• Enables what/if scenarios to be performed in seconds

• Support the monitoring system

• determine alarming thresholds

• Introduction

• Caster SOS and New Requirements


• Visualization



• Summary

• Introduction

• Caster SOS and New Requirements


• Visualization



• Summary

AgendaAgenda

Enhancing the SOS system with PIEnhancing the SOS system with PI• Improved data visualization in real

time

• HMI for transient monitoring

• Supporting tools for historical data analysis

• Improved data visualization in real time

• HMI for transient monitoring

• Supporting tools for historical data analysis

Caster SOSTMCaster SOSTM

Process

OpportunitiesCaster Stability/Quality Prediction/Breakout Avoidance

DataTemperatures

Cooling Water Data

Stopper Rod Position

Heat Transfer

Negative Strip Time

Etc.

Alarms and Warnings through 2 Stability Indices

Top 5 Influences to the Problem

Trending of Data

Thermal Contour Map

InformationAutomatically Slow Cast Speed

Automatically Disposition Product

Strand Operator Slows Down Cast Speed

Operator Deselects Faulty Signal from the Model

Action

DAT SolutionCaster SOS

Process Monitoring FrameworkProcess Monitoring Framework

OpportunitiesQuality/Throughput/Safety

Process

Data

DAT Solution

Information

Action

• Not just limited to caster monitoring

• Applicable in most industries

• Not just limited to caster monitoring

• Applicable in most industries

Batch AnnealBatch Anneal

Process

OpportunitiesConsistent Heating

DataTemperatures

Fuel Rates

Coil Size

Recipe

Etc.

Information

Action

DAT Solution

Paper MachinePaper Machine

Process

OpportunitiesSheetbreak Prevention

DataSpeeds

Drive Currents

Material Properties

Tensions

Etc.

Information

Action

DAT Solution

SummarySummary• Comprehensive

Monitoring Solution

• Utilizes OSIsoft technology

• Internal Development

• Solid Results

• Growth Potential

• Comprehensive Monitoring Solution

• Utilizes OSIsoft technology

• Internal Development

• Solid Results

• Growth Potential

Questions?

Vit [email protected] ext. 2992

Vit [email protected] ext. 2992

Adam [email protected] ext. 3317

Adam [email protected] ext. 3317

Web Sitehttp://dat.dofasco.ca

Web Sitehttp://dat.dofasco.ca

Documents

Dofasco’s Enhanced Monitoring System Vit Vaculik and Adam Smyth OSI PI Users Conference San Francisco May 13, 2003 Vit Vaculik and Adam Smyth OSI PI Users