OPTIMIZATION & ANALYTICAL

OPPORTUNITIES IN THE STEEL

INDUSTRY

BISWAJIT ROYCHOWDHURY

TATA STEEL

IIT D

elh

i –

IBM

Researc

h

Opera

tional R

esearc

h W

eekend

April 9

th2006

Agenda

Conte

xt

-Tata

Ste

el

-In

tegra

ted S

teel P

lant O

pera

tions B

ack G

round

Are

as for O

ptim

ization is S

teel In

dustry

-Pro

cess

-Logis

tics

-C

apacity

An u

nsolv

ed p

roble

m

Challe

nges

45 M

inute

s

Tata Steel Limited

Tata Steel Limited

5 MT fully integrated steel

plant

Key S

egm

ent:Automotive

and Construction

40,000 work force

No#1 Steel Company in the

world-World Steel Dynamics

2006

Fro

m d

irt to

door panels

Inbound

Iron

Coal

Limestone

MRO

Energy

Scrap

Outbound

Distribution

Center

Service

Center

Tier 1

Automotive

Assembly

Appliance

OEM

Torpedo

COAL

Iron Ore

Lime Stone

Imported Coal

Blending Yard

Blending Yard

Calcining

Coke Ovens

Sinter

Other fluxes

Sponge iron

CHARGE

Reheating F

cs Descale

rR

oughin

g M

illC

oil

Box

Descale

rC

ontinuous M

illLam

ella

r

Coolin

g

Coile

rH

R C

oil

Un-c

oile

rLevele

rTrim

mer

Pre

cis

ion

Levele

r

Fly

ing

Shear Bundle

s

HR

Coil

yard P

icklin

gC

old

Rolling M

ill

BAF

Galv

aniz

ing 1

Galv

aniz

ing 2

Skin

pass

Recoiling

Packagin

gC

R c

oil

Yard

Hot M

eta

l

Handlin

g

Desulp

hurization

BOF

RH

LF

Caste

r

Sla

bIn

spection

RsRsRsRs/ ///

− −−−R

oad T

ransport

Rail

Tra

nsport

Ete

rnal Pro

cessin

g

Sto

ck yard

Outb

ound T

ransport

Oth

er transfe

r s

eg

Tube,

Tin

pla

te e

tc

An integrated steel plant is a complex

industrial system in which LIMITED

materials flow through VARIOUS routes to

generate NUMEROUS products through

DIFFERENT series of production units.

Areas of Optimization

OR

orororor

Optimization

!!

Pro

cess

Modelin

g

Capacity

bala

ncin

g

Logis

tics

optim

ization

•Load b

ala

ncin

g o

n

each w

ork

cente

r

•Modelin

g d

em

and

•Pro

duct M

ix

optim

ization

•Invento

ry

optim

ization

•Desig

n b

uffer

•Schedulin

g e

ach

work

cente

r

Etc

..

•Desig

n

transportation

netw

ork

to reduce

freig

ht cost.

•Raw

Mate

rial

movem

ent and

sto

rage.

•WIP

flo

w

bala

ncin

g

Etc

…

Illustrative Scope of Optimization

•Ble

nd o

ptim

ization

of coal m

ix, sin

ter

charg

e, Bla

st

Fces.

•Heat and m

ass

bala

nce for ste

el

make o

pera

tion

•Modelin

g b

low

pattern

in B

OF

•Modelin

g o

f

decarb

urization in

RH

-degasser

•BAF fill

ing facto

r

Etc

…

Process Modeling

Optim

al allo

cation o

f Ele

ctric

al energ

y

•Pro

ble

m–

Energ

y in s

teel pla

nt is

a s

carc

e a

nd e

xpensiv

e resourc

e.

–Typic

ally

easte

rn p

art o

f In

dia

the g

ap b

etw

een the s

upply

and d

em

and

is v

ery

hig

h.

–In

ste

el pla

nt som

e o

f th

e u

nits a

re c

onsid

ere

d a

s e

ssential pow

er

consum

ing u

nits ( s

uch a

s B

F C

O e

tc..) re

quires fix

ed u

nits o

f pow

er.

In case of shortages in the power supply, how the consumption to

be distributed most profitably.

•M

odel O

bje

ctive

–M

axim

izin

g P

rofit contrib

ution

–M

inim

ize the c

ost of pro

duction

–M

axim

ize the T

hro

ughput.

•M

odel used

–Lin

ear Pro

gra

mm

ing w

ith P

ow

er as a

lim

itin

g c

onstrain

t.

Edelman Recognized

Online Slab Temperature Calculation and Control

Problem:

A m

odel to

pre

dic

t the h

eat transfe

r from

the reheating furn

ace

atm

osphere

to

the s

urface o

f th

e s

lab a

nd fro

m the s

lab s

urfaces to the c

ore

of th

e s

lab.

Heating p

rofile

of th

e reheating furn

ace d

epends o

n

•Targ

et dro

p o

ut te

mpera

ture

•Entry tem

pera

ture

•D

iffe

rent th

erm

al pro

pertie

s for diffe

rent gra

des o

f ste

el

•O

pera

tional fluctu

ation in the d

ow

n s

team

pro

cesses

Model Objective:

•U

nders

tand the therm

al m

odel

•U

nders

tand the o

pera

tional re

quirem

ents

•C

alib

rate

the m

odel para

mete

rs

Model Used:

•M

ath

em

atical fu

nctions u

sin

g b

asic

heat transfe

r equations.

Assumptions:

�H

eat transfe

r m

ode is p

rim

arily

radia

tive.

�In

sula

ting e

ffect on h

eat conduction d

ue to s

cale

form

ation is n

eglig

ible

�H

eat transfe

r in

the s

lab is o

nly

fro

m the top a

nd b

ottom

surfaces, m

ode b

ein

g

conduction

�Shadow

ing e

ffect due to s

kid

layout is

sm

all

�Tem

pera

ture

thro

ughout th

e h

orizonta

l pla

n in the s

lab is u

niform

.

Contd..

Online Slab Temperature Calculation and Control

Basic parameters:

Reheating furn

ace o

f w

alk

ing b

eam

type o

f effective therm

al le

ngth

39 m

ete

rs

with m

ax o

pera

ting tem

p 1

350

0C

.

The furn

ace d

ivid

ed into

12 o

pera

ting z

one

-Recupera

tive (to

p a

nd b

ottom

) -P

reheating ( top a

nd b

ottom

)

-Heating (to

p a

nd b

ottom

)-P

resoakin

g (to

p a

nd b

ottom

)

-Soakin

g rig

ht (top a

nd b

ottom

)-S

oakin

g left (to

p a

nd b

ottom

)

Calc

ula

ted n

odal poin

t te

mpera

ture

was e

xtrapola

ted thro

ughout th

e furn

ace

length

to p

rovid

e tw

o c

ontinuous radia

tion c

urv

es

-Alo

ng the c

eili

ng

-Alo

ng the flo

or

This

continuous tem

pera

ture

pro

file

used a

s h

igher te

mpera

ture

and the s

lab

surface a

s a

low

er te

mpera

ture

in the S

tefa

n-Boltzm

an’s

radia

tion e

quation.

This

calc

ula

tes the h

eat flux d

ensity o

f th

e s

lab.

Online Slab Temperature Calculation and Control

Radiativeheat Transfer from the furnace to the slab surface

�N

odal poin

t at each Z

one ( m

axim

um

tem

pera

ture

) w

ere

identified

�M

easure

d tem

pera

ture

by the T

Cs

neare

st to

the n

odal poin

ts u

sed a

s p

rim

ary

ra

dia

tion d

ata

.

�An o

ffset ( fixed a

nd v

ariable

) w

as a

dded to the m

easure

d tem

pera

ture

for

absorb

ing the e

ffect of th

e convective h

eat transfe

r

•Fix

ed p

art-Additio

nal heat transfe

r fo

r th

e s

tandard

fuel flow

at each z

one

•Variable

part-D

evia

tion in c

onvective h

eat transfe

r due to c

hanges in fuel

flow

w.r.t. sta

ndard

.

PI control for achieving required slab temperature

�C

alc

ula

ted s

lab tem

pera

ture

fro

m the 7

layers

then p

rocessed a

savera

ge u

pper

half a

nd a

vera

ge low

er half tem

pera

ture

and c

om

pare

d w

ith the respective s

lab

targ

et te

mpera

ture

.

�The d

evia

tions w

ere

avera

ged for all

the s

labs in a

zone.

�This

was u

sed a

s feed b

ack to P

I control lo

op w

hic

h c

alc

ula

tes the tem

pera

ture

set poin

t fo

r all

Level 1 s

yste

m therm

ocouple

s

�O

ut put dow

n loaded to L

evel 1 s

yste

m

�This

dedic

ate

dly

tries to m

ain

tain

the s

et poin

t by regula

ting the fuel flow

rate

.

Online Slab Temperature Calculation and Control

Conductive heat Transfer from the slab surface to the core of the slab

�The m

odel used o

ne d

imensio

nal Fourier’s h

eat conduction e

quation.

�The tem

pera

ture

and h

eat flux e

quations w

ere

solv

ed u

sin

g fin

ite

ele

ment

techniq

ue

�Sla

b thic

kness w

as d

ivid

ed into

7 layers

each a

t 35m

m a

part.

�Top a

nd b

ottom

surface tem

pera

ture

calc

ula

ted fro

m the radia

tion

equations

was u

sed a

s b

oundary

valu

e.

�Physic

al pro

pertie

s ( s

uch a

s therm

al conductivity, specific

heat etc

..) of th

e

pla

in c

arb

on s

teel w

ere

taken fro

m s

tandard

table

.

�In

terv

al betw

een s

uccessiv

e c

alc

ula

tions w

as taken o

ne m

inute

s.

Logistics Optimization

Cost optimized executable dispatch plan

•Problem

–Tra

nsportation o

f th

e fin

ished g

ood in s

teel in

dustry is

a c

om

ple

x a

nd e

xpensiv

e

opera

tion.

–Typic

ally

easte

rn p

art o

f In

dia

the infrastructu

re is n

ot fa

voura

ble

for th

e

industrie

s.

–R

esourc

es s

uch a

s tra

nsportation m

odes a

re n

ot w

ell

org

anis

ed.

–M

ost of our m

ovem

ents

are

aw

ay fro

m E

aste

rn z

one.

How to use the resources optimally at the lowest cost

•Model Objective

–M

inim

ize the c

ost of finis

hed g

oods m

ovem

ent per to

n.

–M

inim

ize the c

ongestions o

f finis

hed g

oods a

t each n

odes.

–M

axim

ize the m

ate

rial m

ovem

ent.

–Pre

dic

t budget allo

cation requirem

ent.

•Model used

–Lin

ear Pro

gra

mm

ing.

Mother Mill

C u s t o m e r

St.Yd/Hub

EPA

Rail

Road

Container

Products

HR

/ C

R c

oils

/ Sheets

& P

late

s /

Sla

bs

WR

coils

, R

ebars

, Billets

,

Transport

Mode &

Costs

Routes

& Costs

Rail siding

PARAMETERS :

Cost optimized executable dispatch plan

Annual Sales Plan

Product group/ volume/

destination Detailed Annual Business plan

Product group/ Volume / Destination

Total Delivery System Cost

Product group / Route /

Destination / Mode / Cost

data

Transport Capacity

Constraints

Annual Shutdown Plan

Each major resources

L P model

for Cost Optimization

Rework with

Shipment

execution

constraints

Matrix

develo

ped o

n

XL

spre

adsheet.

Cost-optimized & executable Dispatch Plan

Product group / route / mode / destination / volume / cost

Capacity Balancing

•Limited abilityto

proactively fulfill

rapidly-changing

customer demand.

•Infeasible supply plans

due to inability to model

all relevant supply chain

bottlenecks

•Low asset utilization

due to inability to get a

single global view into

entire supply chain

•Sub-optimal costs and

lost salesdue to limited

analytics and

optimization

•Inability to allocate

limited product supply

through sales channels

Problem

•Maximize customer

responsivenessby

integrating supply planning

to strategic planning and

forecasting processes

•Gain global visibilityinto

extended supply chain by

modeling all relevant

production and distribution

bottlenecks.

•Increase feasibilityof

detailed plan by

representing them in global

supply plans.

•Simultaneously optimize

entire supply chain in a real

world fashion by

considering real costs and

multiple goals

•Allocate limited supply as

ATP reservations

Solutions

•Reduced planning

cycle time

•Generation of feasible

plan

•Higher service level

for key customers

•Reduced Inventory

costs

•Reduced

obsolescence cost

•Improved profitability

by allocating most

profitable customer

Benefits

What does the Capacity balancing do to the business ?

Order Planner -Purpose

•The p

rim

ary

goal of O

rder Pla

nner is

to develop a feasible production plan

for

satisfy

ing d

em

and requirem

ents

pla

ced o

n the O

rder (i.e

., c

usto

mer / fo

recast

ord

ers

) respecting the due date promised

•The p

roduction p

lan is d

evelo

ped w

ith the s

imultaneous c

onsid

era

tion o

f th

e

follo

win

g c

onstrain

t ty

pes:

–demand constraints

: due d

ate

s a

nd their tole

rances

–material constraints

: m

ate

rials

availa

bility

(quantities a

nd tim

ing) –

used for

outs

ourc

ed R

M

–capacity constraints

: re

sourc

es a

vaila

bility

(quantities a

nd tim

ing)

•O

rder Pla

nner is

an inte

lligent decision support system

for short-range a

nd

mediu

m-r

ange p

roduction p

lannin

g.

An unsolved problem

Already Existing Body of Work

1.

1.

Modelin

g a

nd S

olu

tion A

ppro

aches to P

ractical Schedulin

g P

roble

mM

odelin

g a

nd S

olu

tion A

ppro

aches to P

ractical Schedulin

g P

roble

ms in

s in

Iron &

Ste

el In

dustry b

y

Iron &

Ste

el In

dustry b

y Dr. Lixin Tang

Dr. Lixin Tang, , Dr. Jiyin Liu

Dr. Jiyin Liu

and

and Professor Zihou

Professor Zihou

Yang

Yang

2.

2.

Ste

el

Ste

el --m

akin

g p

rocess s

chedulin

g u

sin

g

makin

g p

rocess s

chedulin

g u

sin

g L

agra

ngia

nLagra

ngia

nre

laxation b

y

rela

xation b

y Lixin

Lixin

Tang

Tang, Peter B.

Peter B. Luh

Luh, , Jiyin

JiyinLiu and Lei Fang

Liu and Lei Fang

3.

3.

A S

urv

ey o

fA S

urv

ey o

fM

ath

em

atical Pro

gra

mm

ing A

pplic

ations

Math

em

atical Pro

gra

mm

ing A

pplic

ations

in Inte

gra

ted S

teel

in Inte

gra

ted S

teel

Pla

nts

Pla

nts

by

by Goutam

GoutamDutta

Dutta

and

and Robert

Robert Fourer

Fourer

4.

4.

Utilit

y a

nd S

tability

Measure

s for Agent

Utilit

y a

nd S

tability

Measure

s for Agent --Based D

ynam

ic S

chedulin

g o

f Based D

ynam

ic S

chedulin

g o

f

Ste

el C

ontinuous C

asting b

y

Ste

el C

ontinuous C

asting b

y D.

D. Ouelhadj

Ouelhadj , , P. I. Cowling

P. I. Cowling, , S.

S. Petrovic

Petrovic

And there

are

many m

ore

math

em

atical m

odels

develo

ped o

n this

su

And there

are

many m

ore

math

em

atical m

odels

develo

ped o

n this

subje

ct

bje

ct

Already Existing Software Solutions From…

1.

1.

Sie

mens

Sie

mens

2.

2.

Rockw

ell

Auto

mation

Rockw

ell

Auto

mation

3.

3.

IBM

IBM

4.

4.

Bro

ner

Bro

ner

5.

5.

i2i2

6.

6.

PO

SD

ATA

PO

SD

ATA

7.

7.

OM

Partners

OM

Partners

Leavin

g a

sid

e m

any h

om

egro

wn s

yste

ms

Leavin

g a

sid

e m

any h

om

egro

wn s

yste

ms

…….B

ut, th

e d

epth

and b

readth

to c

over fo

r th

e v

ariability

of ste

.But, th

e d

epth

and b

readth

to c

over fo

r th

e v

ariability

of ste

el pro

duction

el pro

duction

as w

ell

as a

ll th

e p

roduction s

teps rem

ain

ed a

question

as w

ell

as a

ll th

e p

roduction s

teps rem

ain

ed a

question ……

....

For in

sta

nce, if c

aste

r and H

SM

are

synchro

niz

ed, th

en the

impact on L

H a

nd R

F a

re ignore

d

How

should

the s

chedulin

g logic

change intra-d

ay if th

ere

is a

change in h

ot m

eta

l qualit

y

Re-ladle

Re-ladle

Vessel

#1

Vessel

#2

Vessel

#3

OLP

OLP

OLP

RH

LF #1

LF #2

Caster#1

Caster#2

Caster#3

Scarfing

Grinding

Inspection

Testing

Direct

For Hot Rolling

Melt Shop Scheduling

Edging

t +

Melt Shop Scheduling

Custo

mers

Ord

ers

Desig

n c

oils

D

esig

n S

labs

Rolling S

ch

Cre

ation

Day R

olling p

lan

Melt s

hop

Capacity

Logis

tics

constrain

ts

Melt s

hop

Constrain

ts

Yes

No

Day S

teel pla

n

Melt s

hop

schedule

LF/R

H a

vaila

bility

Tundis

hLoad

No o

f w

idth

Caste

r health

etc

Level -1

Flo

w

Melt Shop Scheduling

Ste

el pla

n d

epends o

n:

a)D

ow

n s

tream

dem

and b

ased o

n O

rder due

date

b)P

roduction c

onstrain

ts

a)

Technic

al constrain

ts

b)

Shop C

onstrain

ts

c)D

ispatc

h c

onstrain

ts

d)M

axim

ize c

apacity u

tiliz

ation a

t each

resourc

es

e)W

IP reduction

Melt Shop Scheduling

Challenges

Critical Success Factors

••A large % of such initiatives don

A large % of such initiatives don’’ t see the

t see the

light at the end of the tunnel

light at the end of the tunnel ……

……..

….. Due to non-technical reasons

Because

“Technical problems are the easier ones to solve... The soft

issues are always the hardest.”

What Kind of Project is this???

•IT

?

•R

& D

?

•Pro

cess c

hanges ?

•Perform

ance Im

pro

vem

ent?

•Busin

ess S

olu

tion that w

ill s

hape the futu

re ?

Who’s Project is this???

Source: Dr Brian Eck

Low

Hig

h

LowHigh

Understanding of Business

Depth in thinking

Academia

Business

Practitioner

Colla

bora

tion

Business Vision

•C

lear unders

tandin

g o

f

pro

ble

m d

efinitio

n

•Alig

nm

ent w

ith b

usin

ess

strate

gy

•Id

entify

and a

gre

e o

n

direction

•D

efine c

ritical success

facto

rs for th

e b

usin

ess

and p

roje

ct

•U

nify indiv

idual vis

ions

Organizational Issues

•Busin

ess v

isio

n, strate

gy

•Top M

anagem

ent

com

mitm

ent

•C

hange M

anagem

ent

•Scope m

anagem

ent

•U

ser expecta

tions

•R

eportin

g issues

•Tra

inin

g

•Im

ple

menta

tion tim

ing

Manage Expectations

•Expecta

tions, w

hile

necessarily

am

bitio

us,

must be g

rounded in realit

y.

Perform

ance

Perform

ance

Expectations

Expectations

ROI

ROI

Flexibility

Flexibility

Compliance

“I haveto do it this

new way”

Reaction

“I will react to this change if I must”

Avoidance

“I must avoid this change”

Negative perception

“I feel threatened by this

change”

Positive perception

“I see the opportunity in this

change”

Experimenting and Testing

“I will experiment with this change -and

check that it really works”

Action

“I will act to achieve this change”

Commitment

“I wantto do it this

new way”

Awareness

“I am being told about

something”

Understanding

“I know what will change and why”

Engagement

“I see the implications for me/us”

Change can be achieved through

commitment or compliance