Toolbox Integration for Instability
Prediction at Redcar Blast
Furnace, Teesside Cast Products,
Corus UK
www.chem-dss.org
Each site produces
3.2 to 3.6 million tonnes
of liquid iron per year.
All sites use G2.
REDCAR (1 Big Blast
Furnace)
SCUNTHORPE
PORT TALBOT
(2 Medium Blast
Furnaces)
(3 Medium Blast
Furnaces)
Location of Corus UK Blast Furnaces.
The Blast Furnace Process
Sinter,ore
and coke
Hot blast
1100oC
4 bar
Carbon
bricks
Top gas
2 bar/120oC
21%CO
21%CO2
5% H2
Steel shell
Water cooled
pressure vessel
Iron and
Slag
1500oC
Melting Zone
Melting
Zone
= Passage of
Reducing gas
14 Rows of
Cooling Staves
48 – 60 staves
Per row
The Objective


Predict aerodynamic instability in order to enable the
controller to reduce the blast volume in time to reduce the
effect.
Effect usually seen as
 Sudden slip of the material in the furnace, which can lead
to a Surge of gas at higher than normal pressure through
the furnace stack, hence lifting the pressure relief valves.
 Channelling of gas through the burden which can lead to
high local heat load onto the furnace wall cooling plates.
 Poorer gas distribution in the furnace hence reduction in process efficiency.

Blast Furnace process:
Combinations of Toolboxes







iMSPC alone
iMSPC with Qualtrend and G2 rules to analyse sequences of episodes
iMSPC with SALSA
Qualtrend with SALSA
(iMSPC is a multivariate SPC toolbox by Computas, Norway. G2
Qualtrend is a qualitative trend analysis toolbox by University of Girona.
G2
 Generation of objects, known as episodes, from a univariate signal
Salsa is a pattern recognition toolbox by University of Toulouse. Labviews
Toolboxes communicate using XML Blaster, freeware,
using A G2 module as the ‘data store’

G2
G2
PC1,
PC2
iMSPC
PC1,
PC2
Raw data
Qualtrend
Episodes
G2 rules
Analyse sequences
of episodes
iMSPC
Contribution analysis
On PC1, PC2. Alarm
PC1, PC2,
PC3, SPE
Salsa
Classification
Qualtrend
Episodes
Salsa
Classification
Data updated
Every minute
iMSPC Alone
iMSPC
iMSPC
Raw data
Principal
Components
Contribution
Analysis
Principal Component Analysis
Data compression without loss of information
Smaller number of new variables generated called
‘Principal Components’
i.e., reduce dimensionality of the data
Each principal component is a linear combination of the
original normalised variables
Variables Selected for PCA
Stability Index
NW Row 6 to Row 9 differential pressure (Quadrant 1)
NE Row 6 to Row 9 differential pressure (Quadrant 2)
SW Row 6 to Row 9 differential pressure (Quadrant 3)
SE Row 6 to Row 9 differential pressure (Quadrant 4)
CO utilisation [100 * CO2/(CO + CO2) in off gas]
Sum of CO + CO2 in off gas
Permeability
These 7 selected after much testing with many other
variables
Top gas
Composition,
pressure
Row 6 to 9
DP over
4 quadrants
Permeability =
f(blast pressure,
top pressure,
blast volume)
Wall pressure
tappings
Blast pressure
temperature
volume
Blast Furnace Signals used for PCA Models
Calculation of principal
component scores
PC1 =
0.26 * CO Utilisation
+ 0.40 * Permeability Resistance
+ 0.063 * (CO + CO2)
+ 0.47 * Row 6 to 9 DP Quadrant 1
+ 0.45 * Row 6 to 9 DP Quadrant 2
+ 0.45 * Row 6 to 9 DP Quadrant 3
+ 0.38 * Row 6 to 9 DP Quadrant 4
PC2 factors
-0.33
-0.26
0.81
0.06
0.13
-0.16
0.31
Variables must be normalised:
Normalised value = (actual value - mean) / standard deviation
Mean and standard deviation derived from stable period of
operation
We use an adaptive mean
iMSPC Model Configuration in G2
Link to
model
Inputs
updated
every
minute
Calculate
5 minute
moving
average
Inputs to
model
Outputs
from
model
Outputs to
G2 object
(to
Qualtrend)
iMSPC Alone
iMSPC
iMSPC
Raw data
Principal
Components
Contribution
Analysis
iMSPC Contribution Analysis

Contribution Analysis monitors the bi-variate trend of PC1 v
PC2 (These 2 PC’s represent 70% of the variability in the
data)
 Identifies which variables have contributed the most to the
change in principal component.
 Alarm if 6/7 points outside action limit and significant
change in at least 1 quadrant for 6-9 Differential Pressure.
Blast Furnace Wall Pressure trends
1
Row 6 - 9 DP
17 aug 03 09:00
0.8
0.6
0.4
0.2
Q. 1
Q. 3
Q. 2
Q. 4
0
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
Warning message
Yellow region outside warning limi
Pink outside action limit
Contribution Analysis: 6/7 points outside Action limit
12:50 Blast volume reduced for poor permeability
13:30 1.5m slip
14:10 2m slip
iMPSC with Qualtrend and G2 rules
Qualtrend
iMSPC
Episodes
Raw data
PC1, PC2
Sequence of episodes
analysed in G2 procedure
Filter
Data entry (PC1)
Attributes of current
episode.
List of past episodes
Range check
Configure
attributes to be
stored in episodes
and hold current
values
Filtered signal
7
Calculate 1st derivative
6
16
31
First derivative
Limits
Signal block (level = normal/low)
Episode Types:
Type
7
6
16
31
Level
First derivative
Normal
Normal
Normal
Low
Low
Low
Low
Normal
Qualtrend: development of rules
22 * 24 hour periods of 1 minute data supplied to UDG from
Jan 2002 to Oct 2003.
PC1 and PC2 Episodes generated in Qualtrend.
Sequences of episodes analysed.
Possible rules tested in Matlab.
Successful rules programmed into G2 and run on line at Redcar
since October 2003.
Within the same G2 as iMSPC. (The live plant G2).
G2 Rules

Rule 1 looks for a sequence of episode types from PC1.
 Criteria set for minimum rate of change (slope) and
degree of change (amplitude).

Another rule looks for a similar sequence of episodes from
PC2, and generates an alarm if the most recent episode from
PC1 satisfies certain conditions.
 Effectively, this detects a sequence of events in the
process.
To prevent false alarms, an ‘enabler’ has been added based on
the recent trend in heat flux.

Filtered PC1
Episode Types:
Type Level
First derivative
6
Normal
Low
31
Low
Normal
First derivative
Current episode = 31 and
Max-min of previous episode > 2.2
And min slope < -0.0015
2m Slip at 09:40. 40 minutes warning.
Filtered PC2
Episode Types:
First derivative
Type Level
First derivative
6
Normal
Low
31
Low
Normal
PC2 Current episode = 31 and
previous episode = 6
Min slope of last episode of PC1 < -0.0015
And finished within 10 minutes
2m Slip at 09:40. 35 minutes warning. Confirms previous message
Summary Statistics
Event
Type
Number
of
events
Predicted
by iMPSC
alone
Predicted by
iMSPC/
Qualtrend
PC1
Predicted by
iMSPC/
Qualtrend
PC1/PC2
Not
predicted
Major
19
8
13
6
0
Minor
10
1
1
2
7
Events detected over 22 days Jan 2002 – Oct 2003.
Classed as predicted if more than 10 minutes warning.
Major event: Slip >=1m and/or excessive heat flux.
Minor event: Smaller slip and/or significant rise in heat flux.
Sometimes alarms also generated during event (high heat flux).
Conclusion

All of the major events were predicted (19/19)

Only 3/10 of the minor events were predicted.
 However, it is unlikely that action would have been taken
for minor events.
iMSPC with Salsa
Raw data
G2
Windows
iMSPC
SALSA
PC1, PC2,
PC3, SPE
Classification to
Normal,
Pre-slip or
Slip
iMSPC and SALSA

Same data as used in for iMSPC/Qualtrend/G2 rules
 (PC1 – PC4, SPE and T2 for 22 * 24 hour periods)

Best classification gained with PC1, PC2, PC3 and SPE
However, too many false alarms

Raw data with Qualtrend and Salsa
G2
Windows
Qualtrend
Salsa
Episodes
Raw data
(4 * differential
Pressures)
Classification to
Normal,
Pre-slip or
Slip
Raw data with Qualtrend and
SALSA


Classification based on data from early 2002.
Classification based on
 Quantitative values (values at end of previous episode)
 Qualitative values (current episode types)
 So 8 inputs (4 differential pressure signals: 4 sets of episodes)
Can give more advanced warning than other methods described.
e.g., 4 Jan 2002. 30 mins before iMSPC/Qualtrend.
Issues
SALSA on-line reliability – stalls after a day.
Need to write classifications back from SALSA to DTM.
Row 6-9 Differential Pressure. 3-4 Jan 2002
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.112:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00
-0.2
-0.3
= 2 metre Slips
SALSA
alarm
Qualtrend
Alarms
iMSPC alone on 4th Jan 2002.
Did not exceed action limits for 6 minutes, so no alarm
G2
G2
PC1,
PC2
iMSPC
PC1,
PC2
Raw data
Qualtrend
Episodes
G2 rules
Analyse sequences
of episodes
iMSPC
Contribution analysis
On PC1, PC2. Alarm
PC1, PC2,
PC3, SPE
Salsa
Classification
Qualtrend
Episodes
Salsa
Classification
Blast Furnace process
Summary of Results

1. iMSPC alone



2. iMSPC with Qualtrend and G2 rules to analyse sequences of episodes



Predicted remaining major events and very few false alarms once heat flux trend ‘enabler’
added
1 and 2 predicted all the major events.
3. iMSPC with SALSA


All alarms generated by action limits are valid
Many events are missed
Many false alarms
4. Qualtrend with SALSA



Predicts certain types of faults with good warning
Salsa not robust enough for continuous on line
Salsa needs to send classifications back to DTM