1

P009-Use of data mining techniques for better

insights of iron making processes at Tata Steel

Team1.Arunabh Bhattacharjee(Speaker)2.Shambhu Tiwary3.Ashish Chakravorty

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?

Fast ,smart and evidence based decision making

Answer

Predictive analytics & data mining

Business Intelligence is a set of technologies and processes

that use data to understand and analyze business

performance.

Predictive analytics encompasses statistical techniques , data

mining models, text mining etc

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

Predictive analytics

Business Intelligence

Business Intelligence & Analytics

Degree of Intelligence

Co

mp

eti

tiv

e

Ad

va

nta

ge

Standard Reports

Ad hoc Reports

Query Drill down

Standard Reports

Statistical analysis

Alerts

Predictive Modeling

Forecasting/Exploration

Optimization

Access & reporting

Analytics

Business valueComplexity level increases with business value

High Value, High

Complexity

Business

Understanding

Data

Preparation

Data

Understanding

Data

Modeling

Evaluation

Deployment

Data

Advantages

Industry neutral

Tool independent

Cross Industry Standard Process for Data Mining (CRISP-DM)

Internationally recognized methodology

Application Case

Reduce NH3 in Clean coke oven gas

Oven

Heating Chamber

Cle

an g

as

Gas to by product plant

Coke Oven (C O ) Gas is used as a fuel gas •Coke Plant

•Rest of the steel plant

Ammonia (NH3) is highly corrosive

Function of by Product Plant is to

Clean Coke Oven gas

Remove impurities like tar, ammonia and

naphthalene etc. from the gas

Coke Oven

CONDENSATE TANK

COOLING WATER

CHILLED WATER

EXH

INCINERATOR

NAPTHELENE VAPOUR

**TOFOUL GAS

MAIN

LIQUORSTORAGE

BIOLOGICAL OXIDATION TREATMENT PLANT

GASHOLDER

BOOSTER

BATTERY HEATING

W. P. BOOSTER

FL. LIQ.

P. S. A.S. N.S.

TAR

P.T. Pt

TAR DECANTER.

AMMONIA STILL NAPTHALENE

STILL

**C O Gas ETPPC DC

PC DC Primary cooling and deep coolingP S Pre scrubbingA S Ammonia scrubbing

Schematic Layout of Coke by product plant

1 2 3

StrippedLiquorTank

Rich Liquortank

AMMSTILL

S/L CHILLER

IncineratorC.O. GAS

AmmoniaScrubbers

PreScrubbers

Rich/lean liquor heat exchanger

S/L COOLER

P

SLF

95 C

70 C

35 C 70 C

Rich Liquor

StrippedLiquor

1.20 gm/100 C.C

.005gm/100 C.C

40 C

30 C

40 C

Steam

P

P

SLT

SLF Stripped liquor flow

SLT Stripped liquor temperature

Ammonia Removal Circuit

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.

Process Requirement-Key challenges?

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

  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

SLC 0.05 0.03 0.01 0.08 1.00 -0.10 -0.13 0.00

SLF -0.14 -0.14 -0.14 0.04 -0.10 1.00 -0.01 -0.05

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

Correlation Matrix

Correlation matrix shows that gas scrubber temperatures have a direct impact on ammonia in C O gas

Data Modeling

Find the most

important

parameters

impacting the NH3

in clean C O gas.

Predict

generalized rules

using ANN for

NH3<40

Enlarged View

Out of the many rules generated by

the algorithm the rule which

predicts the condition when

NH3<40 is selected

ANN Prediction

22-22.533-33.5

0.005-0.006 52-53

ANN Prediction

NH3<=40

                   

T + GT1 off

                   

T + GT2 off

                   

T + GT3 offT  22-22.5 T   T   T  

GT1  30-30.5 GT1   GT1 30-30.5 GT1 30-30.5GT2 29.5-30 GT2 28.5-29.5 GT2   GT2 29.5-30.0GT3 29.5-30.5 GT3 29-30 GT3 29.5-30 GT3  SLF 52-55 SLF 54.0-55.0 SLF 52.0-53.0 SLF 52-54SLC 0.005-0.006 SLC 0.005-0.006 SLC 0.005-0.006 SLC 0.005-0.006SLT 33-33.5 SLT 33.5-34 SLT 33-33.5 SLT 33-33.5

NH3 10.0-40.0 NH3 10.0-40.0 NH3 10.0-40.0               

T off GT1 off GT2 off GT3 offT   T 22-22.5 T 22-22.5 T 22-22.5

GT1 30-30.5 GT1   GT1 30.0-30.5 GT1 30-30.5GT2 29.5-30.0 GT2 29.5-30.0 GT2   GT2 29.5-30.0GT3 29.5-30.5 GT3 29.5-30.0 GT3 29.5-30.5 GT3  SLF 52-54 SLF 52-54 SLF 52-54 SLF 52-54SLC 0.005-0.006 SLC 0.005-0.006 SLC 0.005-0.007 SLC 0.005-0.006SLT 33-33.5 SLT 33-33.5 SLT 33-33.5 SLT 33-33.5

NH3 10.0-40.0 NH3 10.0-40.0 NH3 10.0-40.0 NH3 10.0-40.0

Prediction of Operating range at different conditions

Confirmation of effectsA

mm

on

ia(m

g/N

m³/

hr)

Am

mo

nia

(mg

/Nm

³/h

r)

Based on the data mining results

standard operating procedures(SOP)

were revised which further

strengthened our daily management

practices at shop floor

Major shut down work at by product plant

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

Conclusions

End of presentation

Thank You!