Implemented Innovation on Predictive Analysis Method for...

1SlideTata SteelTata Innovista

ROLLING MILL OPERATING COMMITTEE - 2019

Implemented Innovation on Predictive Analysis Method

for Enhancing Productivity- A Multi Dimensional Approach

Presented By: Pankaj Kumar

New Bar Mil, Tata Steel

Date: 13 February 2019

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D

Content

Challenges & Overcoming Them

A Trigger for Innovation

B

C Journey of continuous improvementC

D Journey of continuous improvementD

Specific areas of focus – target 750+

Implementation

TBEM 2018

Confirmation of Effects and Way ForwardE

Novelty / Uniqueness

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A. Trigger for Innovation

R Factor = Area(in)

Area(out)

Incorrect R Factor may lead to:

1. Tension or Compression which

can cause cobble in mill

2. Cobble in mill

3. Abnormal load on mill stands

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B. Challenges and Overcoming Them (1/2)

Challenges with R Factor Control:

• Operator has to take decision by monitoring thousands of Data Points every hour.

• Operator adjusts R Factor based on his best judgement.

• While adjusting the potential of a wrong decision is as high as 13%. .

Correct Decision

Incorrect Decision

OEM has no solution available for this problem.

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B. Challenges and Overcoming Them (2/2)

Processing of millions of data Institutionalize Human Knowledge is

key to the problem resolution

Data Analytics was used to

generate meaningful solution

Capturing Operator

experience and

defining Business

Rule

Formation of 18 Dimensional Matrix

through Multivariate Analytics

Determining the regions of high purity using

K Means Clustering

Plane drawing to segregate the good and

bad clusters through Logistic Regression

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C. Implementation

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D. Novelty / Uniqueness

R Factor

Cluster

Operator

independent

Online

visualization

• 1st in world to use R Factor clusters for mill setting.

• Option to choose from 5 different clusters for every section

• Similar R Factor settings in all shifts

• Quantitative capturing of Operator knowledge

• Online R Factor trend available

• Mill settings done based on R Factor

• Data categorized in green-yellow-red based on deviation

from selected cluster

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E. Confirmation of Effects and Way Forward

• Before : on horizontal axis includes delays due to abnormalities in R

factor during the period H2 FY’18 for which campaign wise data is

taken to train the model.

• After : on horizontal axis indicates the delay due to abnormal R

factors over a period H1 Fy’19 which is seen to decline by 34% when

calculated as avg delay in min per day during the period.

• This would lead to a reduction of delay by 7.7 hrs per month which

would lead to incremental savings of Rs 3.22 Cr Annually

1. Inter-stand head tracking time difference

2. Mill Shear (S8 and S16) intelligent monitoring

3. Looper actuation tracking

4. Loop Height abnormality detection

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Thank You

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New Bar Mill: Process Flow

COOLING BED

WALKING BEAMREHEATING FURNACE

ROUGHING MILLSTANDS #1 - #8

INTERMEDIATE MILL #1STANDS #9 - #12 INTERMEDIATE MILL #2

STANDS #13 - #16

CROPPING AND DIVIDING SHEAR

NO TWIST MILLSTANDS #17 - #22

WATER BOX #1, #2

LINE A

LINE B

LINE A

LINE B

BRAKING PINCH ROLLS

COLD SHEAR

STRAPPING MACHINES #1 - #7WEIGHING AND UNLOADING STATION #2

WEIGHING AND UNLOADING STATION #1

BILLET CHARGING BED

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Praveen Thampi

NBM, Analytics Project

Manager

Abhishek Raj

NBM, Business Translator

Pankaj Kumar

NBM, Business

Translator

Sushil Kumar Tripathy

NBM, Operations

Translator

Rahul Anand

Data Scientist

Abhimanyu Kumar Singh

ITS, Data Engineer

Sanjeev Kumar

Automation, Data Architect

Rahul Anand

Visualization Designer

Abhimanyu Kumar Singh

ITS, Visualization Designer