P009-Use of data mining techniques for better
insights of iron making processes at Tata Steel
Team
1.Arunabh Bhattacharjee(Speaker)
2.Shambhu Tiwary
3.Ashish Chakravorty
1
Agenda

Why do you need data mining?

Data mining and business intelligence

Business value

Methodology

Application Case
Reduce NH3 in clean coke oven gas
 Conclusions
Why do you need data mining?
•Companies offer similar products & services using comparable technology
•Proprietary technologies rapidly copied and breakthrough innovation is not
always possible
•Geographical location & protective regulation is not always an advantage
What is then the key differentiating factor?
How do you get the competitive advantage?
Answer
Fast ,smart and evidence based decision making
Predictive analytics & data mining
Business Intelligence is a set of technologies and processes
that use data to understand and analyze business
performance.
Business Intelligence
Predictive analytics encompasses statistical techniques , data
mining models, text mining etc
Predictive analytics
Data Mining is the process of extracting valid, useful,
unknown, and comprehensible information from data
Predictive analytics is used interchangeably with data mining
Term has become popular and used by most IT vendors
Data
Mining
Business Intelligence & Analytics
Optimization
Predictive Modeling
Analytics
Forecasting/Exploration
Competitive Advantage
Statistical analysis
Alerts
Standard Reports
Access &
reporting
Query Drill down
Ad hoc Reports
Standard Reports
Degree of Intelligence
Business value
Complexity level increases with business value
High Value,
High
Complexity
Cross Industry Standard Process for Data Mining (CRISP-DM)
Internationally recognized methodology
Business
Understanding
Advantages
Data
Deployment
Industry neutral
Understanding
Data
Tool independent
Data
Evaluation
Preparation
Data
Modeling
Application Case
Reduce NH3 in Clean coke oven gas
Coke Oven
Function of by Product Plant is to
Coke Oven (C O ) Gas is
used as a fuel gas

Clean Coke Oven gas

Remove impurities like tar, ammonia and
• Coke Plant
naphthalene etc. from the gas
• Rest of the steel plant
Gas to by
product plant
Oven
Heating Chamber
Clean gas
Ammonia (NH3) is highly
corrosive
Schematic Layout of Coke by product plant
BATTERY HEATING
CHILLED
WATER
COOLING
WATER
GAS
HOLDER
BOOSTER
EXH
C O Gas
**
PC DC
ETP
P. S.
A.S.
N.S.
W. P.
BOOSTER
FL. LIQ.
TAR
DECANTER.
CONDENSATE
TANK
AMMONIA
STILL
TAR
NAPTHALENE
STILL
LIQUOR
STORAGE
PC DC Primary cooling
and deep cooling
P.T. Pt
PS
Pre scrubbing
AS
Ammonia
scrubbing
BIOLOGICAL OXIDATION
TREATMENT PLANT
NAPTHELENE
VAPOUR
INCINERATOR
**TO
FOUL GAS
MAIN
Ammonia Removal Circuit
Rich Liquor
C.O. GAS
1.20 gm/100 C.C
1
Pre
Scrubbers
2
3
Ammonia
Scrubbers
SLT
30 C
AMM
STILL
Rich
Liquor
tank
Steam
P
S/L CHILLER
SLF
Stripped
liquor flow
SLT
Stripped
liquor
temperature
P
40 C
Stripped
Liquor
Tank
SLF
70 C
35 C
P
Stripped
Liquor
Incinerator
40 C
95 C
S/L COOLER
.005gm/100 C.C
70 C
Rich/lean liquor heat exchanger
Process Requirement-Key challenges?
1.Find out the range of parameters, which would keep ammonia in clean gas to
below 40, by taking out one parameter one-by-one.
2.As a next step, take out PCDC temperature, in combination with scrubber
temperatures (first T+GT1, then T+GT2, and finally T+GT3) and see the effect on
other parameters.
3.Finally, take out T, GT1, GT2, and GT3, and see what should be range of the
remaining parameters.
Data Preparation
For a more comprehensive analysis following key parameters were considered viz

Gas scrubber temperatures(GT1,GT2,GT3)

Gas temperature(T) after Primary Cum deep cooling (PCDC)

Stripped liquor flow (m³/hr)

Stripped liquor Conc. (mg/100cc)

Stripped liquor Temp.(ºC)

Ammonia in clean C.O. gas(mg/Nm³/hr)
 More than 2 years of data (FY13, FY15) have been used.
 Final subset of data was then treated for missing values, outliers etc.
 Multiple iterations .
 Maximum amount of time and effort was spent at this stage.
 Total volume of data =10000
Data Modeling
Correlation Matrix
 Find the most
GT1
GT2
GT3
NH3
SLC
SLF
SLT
T
GT1
1.00
0.91
0.75
0.53
0.05
-0.14
-0.11
0.65
GT2
0.91
1.00
0.86
0.43
0.03
-0.14
-0.05
0.47
GT3
0.75
0.86
1.00
0.39
0.01
-0.14
0.28
0.34
NH3
0.53
0.43
0.39
1.00
0.08
0.04
0.08
0.43
generalized rules
SLC
0.05
0.03
0.01
0.08
1.00
-0.10
-0.13
0.00
using ANN for
SLF
-0.14
-0.14
-0.14
0.04
-0.10
1.00
-0.01
-0.05
NH3<40
SLT
-0.11
-0.05
0.28
0.08
-0.13
-0.01
1.00
-0.12
T
0.65
0.47
0.34
0.43
0.00
-0.05
-0.12
1.00
important
parameters
impacting the NH3
in clean C O gas.
 Predict
Correlation matrix shows that gas scrubber temperatures have a direct impact on ammonia in C O
gas
ANN Prediction
Out of the many rules generated by
the algorithm the rule which
predicts the condition when
NH3<40 is selected
Enlarged
View
ANN Prediction
33-33.5
22-22.5
0.005-0.006
52-53
Prediction of Operating range at different conditions
NH3<=40
T
22-22.5
GT1
30-30.5
GT2
29.5-30
GT3
29.5-30.5
SLF
52-55
T + GT1 off
T
GT1
GT2
28.5-29.5
GT3
29-30
SLF
54.0-55.0
T + GT2 off
T
GT1
30-30.5
GT2
GT3
29.5-30
SLF
52.0-53.0
T + GT3 off
T
GT1
30-30.5
GT2
29.5-30.0
GT3
SLF
52-54
SLC
SLT
SLC 0.005-0.006
SLT
33.5-34
NH3
10.0-40.0
SLC 0.005-0.006
SLT
33-33.5
NH3
10.0-40.0
SLC 0.005-0.006
SLT
33-33.5
NH3
10.0-40.0
GT1 off
T
22-22.5
GT1
GT2
29.5-30.0
GT3
29.5-30.0
SLF
52-54
GT2 off
T
22-22.5
GT1
30.0-30.5
GT2
GT3
29.5-30.5
SLF
52-54
GT3 off
T
22-22.5
GT1
30-30.5
GT2
29.5-30.0
GT3
SLF
52-54
SLC 0.005-0.006
SLT
33-33.5
NH3
10.0-40.0
SLC 0.005-0.007
SLT
33-33.5
NH3
10.0-40.0
SLC 0.005-0.006
SLT
33-33.5
NH3
10.0-40.0
0.005-0.006
33-33.5
T off
T
GT1
GT2
GT3
SLF
30-30.5
29.5-30.0
29.5-30.5
52-54
SLC 0.005-0.006
SLT
33-33.5
NH3
10.0-40.0
Confirmation of effects
Based on the data mining results
Ammonia(mg/Nm³/hr)
standard operating procedures(SOP)
were revised which further
strengthened our daily management
Ammonia(mg/Nm³/hr)
practices at shop floor
Major shut down work
at by product plant
Conclusions
 In addition to the well known areas like marketing & sales, fraud detection etc.
data mining can also be used in complex processes like that of iron and steel
making.
 Data mining is a very intelligent technique to get meaningful insights from large
volumes of data in just few seconds.
 Data mining can be a key differentiator in fast and evidence based decision
making
End of presentation
Thank You!