Using Simulation Approach to move from Manual to Real-Time Autonomous Scheduling for a Batch Heat Treatment Process

Steve ThorntonScientific Fellow - Through Process IntegrationTata Steel Research and Development

July 9, 2014

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Agenda

• Tata Group

• Tata Steel

• Tata Steel Speciality

• Through Process Integration

• Knowledge Engineering

• Data Mining

• Simulation

• TSB AUTOPLAN Project

• Deployment into Real Time Operations

• Summary and Questions

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A Part of the Tata Group

• In 2007 Tata Steel Limited acquired Corus Group plc

• On 27 September 2010 Corus rebranded to Tata Steel

• Tata Group is one of the world’s fastest growing and most respected corporations

• Tata’s businesses span seven major industry sectors: engineering, communications and IT, materials, services, energy, consumer products and chemicals

• Tata is India’s largest private sector employer and has over 540,000 employees in over 100 countries.

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Tata Steel

• One of the world’s top 500 companies

• A top 10 global steel producer and the second most geographically-diverse steel company

• Annual crude steel capacity of 28 million tonnes

• Tata Steel employs more than 80,000 people across five continents

• Manufacturing operations in 26 countries and customers in more than 50 markets worldwide

• Turnover in 2012-13 was $22.5 billion

• A major part of the Tata Group

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Key Facts – Deliveries, Turnover and EBITDA

Expansion at Jamshedpur + New Steel Works at Kalinganagar in Odisha will add 6Mtpa to Tata Steel India by 2015

[1 Rs. crore = Rs. 10,000,000 = $166,852 - 134,712 Crore = $22.5bn]

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Through Process Integration - Tools

KnowledgeEngineering

DataMining

DiscreteEvent

Simulation

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Through Process Integration

HM DeS Conv STIR LAF VDG CC4 PEN RHT ROLL FIN TEST

Liquid Solidify Shape

HM Hot Metal (95% Fe) from Blast Furnace

DeS Desulphurisation ProcessConv Convert to Steel (99% Fe)STIR Stir and Add Alloys

LAF Electrical Heating and fine tuning of composition

VDG Vacuum Degas to remove Hydrogen

CC4 Continuous casting to solid form of ‘Blooms’

PEN Slow Cooling for solid state dehydrogenisation (if required)

RHT Reheat to 1250°C for rolling process

ROLL Rolling to elongate and change profile to required section shape

FIN Finishing Processes, e.g. straightening

TEST Pre-dispatch product assessment of internal and surface quality

Example – Manufacturing Process for Rail – Prediction of Final Product Internal Quality and Application to Process Improvement

• Time scales – between 2 and 8 weeks

• Decoupled Processes

• Different Technologies (Manufacturing and IT)

• Generally individual processes not well integrated (People and Data)

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Integration Technologies

KnowledgeEngineering

DataMining

DiscreteEvent

Simulation

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Knowledge Engineering – Mapping the Landscape

• What is Knowledge ?(Often intangible) is what is applied to go from data and information, to a decision and/or action.

• What do we need to achieve (value to the business) ?

• What Knowledge do we need ?• What Knowledge do we have ?• Where is it ?• Is the Knowledge Base secure ?• Are we applying our Knowledge effectively ?• Could we do it differently ?• Could we do something else ?

• Knowledge about knowledge is the raw material for business improvement

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Knowledge Guided Data Mining – Workshop Templates

KPI #1What is the Current Level of Performance ?

What are Aspirational Levels of Performance ?

Concepts Influences Measures Data Tasks

Process 1

Process 2

Process 3

Process 4

Process 5

Process 6

Process 7

Process N

• Template to Support Knowledge Capture from different process perspectives• Opportunity to pose cross-process questions and concerns• Identify needed data rather than starting with ‘what have we got’• Develop collaborative knowledge framework integrating KM with Analytics

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Integration Technologies – Tools in the Box

KnowledgeEngineering

DataMining

DiscreteEvent

Simulation

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Data Mining – Is it Cheating to Ask ?

Action

Data

Information

Knowledge

Finishing MillEqualising Furnace

260 m

Coiler

CoolingBed

CastingMachine

75% of an Organisation’s Knowledge is ‘hidden’in Data and People !

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Knowledge Guided Data Mining – Workshop Templates

KPI #1What is the Current Level of Performance ?

What are Aspirational Levels of Performance ?

Concepts Influences Measures Data Tasks

Reheating Differentials in heating rates at surface and in centre results in stresses which could open up voids, especially towards the ends of the blooms where heat input through ends could also be an issue

Voids formed due to oxidation of silicates and/or excessive porosity in centre of bloom

Reheating rates and delays during processing.

Rates of progressions through different sections of the furnace

Centring of bloom on the beams in the furnace

Transient casting speeds during final solidification

Furnace residence times

Delays in different zones

Temperature differentials centre to surface

Visual records of bloom positioning in furnace

Speed profile from mould to unbending point

Level 2 data from furnace control model, harvesting into MSMLive imminent

Furnace charge and discharge times and thus furnace residence

Casting speed from PI data repository at Steelplant.

Harvest MSMLive tables

Arc_bloom

Arc_bloom_hist

Align snapshot timestamps with discharge times to find matches with time based data

Analyse Speed signals for each strand to compute speed profile through machine and derive parameter to represent.

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Data Mining – Making Data Earn Its Keep

Multi-Process, Multi-Variable, Time shifted

•Finding Patterns in Your Data•Which you can Use•To do Business Better

•Tools and Techniques for effective combination of business knowledge and data

•Hunching not Crunching

•Highly collaborative approach requiring knowledge engineering as well as data analysis skills

•BIG DATA – Volume, Velocity, Variety

•Real-Time Analytics – Pattern Recognition, Rule Induction etc.

The “Latest”

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Data Exploitation Maturity Curves

Where Next ?

time

Fun

ctio

nalit

y

Data Capture / Measurement

Data Integration / Product Tracking

Business / Supply Chain Integration

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Data Exploitation Maturity Curves

time

Fun

ctio

nalit

y

Data Capture / Measurement

Data Integration / Product Tracking

Business / Supply Chain Integration

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Integration Technologies

KnowledgeEngineering

DataMining

DiscreteEvent

Simulation

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Discrete Event Simulation (DES) - Characteristics

• Computer based technique for building models of real-life systems

• Which exhibit behaviour approximating that of the real system which can incorporate natural variability

• Can deal with complex systems which are difficult or impossible to model rigorously [But cannot recreate “reality”]

• Allow possible outcomes of a scenario to be investigated and thus assessment of risk and robustness

• Stimulates knowledge capture and ideas generation

• Achieve shared insight and good decisions, more quickly

• Permit simplification of existing situation to provide opportunity to throw away knowledge legacy

Experiment with your business in the safe virtual world of the computer

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Simulation Project – Why ?

Interviews and Data Collection

Model Building and Validation

Experimentation and Recommendations

Extension(If required and have time)

Iteration

Verification

25% 25% 50%

Start with Objectives !

What do we want to achieve ?Why do we need a model ?What will we do with it ?

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Simulation Project Process – Questions to Insight

Interviews and Data Collection

Model Building and Validation

Experimentation and Recommendations

Extension(If required and have time)

Iteration

Verification

25% 25% 50%

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How Real Does it Need to Feel ?

As real as it needs to be (to gain confidence of target audience)

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Applications of Simulation Approach

Lean Manufacturing• Explore impact on coupled processes and develop business logic

Restructuring • New Plant design and Business configuration, investment and operations decisions

Optimising Logistics in line with configuration changes• To ensure continued service for example in conjunction with throughput changes

Scheduling Applications• Capturing scheduling knowledge and application for real-time decision support

Product Application Strategy• Alternative product application strategy, move decisions downstream

Supply Chain Optimisation• Changing scheduling and stock policies to reduce supply chain inventory for major

supply chains

Overall, develop simulation models for experimentation in safe virtual world, and routine application for decision support

Achieve Shared Insights about the ‘As-Is’ and ‘Could-Be’

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TPI – Development Directions

Knowledge Engineering > Remove Risk > Improve Processes > Exploit Capability > Knowledge Systems

Data Integration > Data Mining > Supply Chain Monitors > Order Fulfilment > Decision Support Tools

Simulation > Operational Strategy > Information and technology Needs > Scheduling Support > Supply Chain Development > Real-Time Application

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Tata Steel in Europe

• Second largest steel producer in Europe

• Crude steel capacity of 20 mtpa

• Approximately 35,000 employees worldwide

• Major manufacturing sites in the UK, the Netherlands, Germany, France and Belgium

• Supplier to the most demanding markets:

• Automotive• Construction• Packaging• Energy & Power• Material Handling• Consumer Goods• Engineering• Rail• Shipbuilding• Aerospace• Defence & Security

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Tata Steel in Europe

• Second largest steel producer in Europe

• Crude steel capacity of 20 mtpa

• Approximately 35,000 employees worldwide

• Major manufacturing sites in the UK, the Netherlands, Germany, France and Belgium

• Supplier to the most demanding markets:

• Automotive• Construction• Packaging• Energy & Power• Material Handling• Consumer Goods• Engineering• Rail• Shipbuilding• Aerospace• Defence & Security

Tata Speciality Main Sectors

Carbon SteelsAlloyedStainless

Heat Treated

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Qualities of Steel Offered by Tata Steel Speciality

• Alloy through-hardening, case-hardening and nitriding steels

• Carbon and carbon-manganese steels

• Micro-alloyed steels

• Stainless steels

• High quality aerospace grades

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Manufacturing processes

• Melting

• Continuous bloom casting

• Ingot Casting

• Re-melting

• Primary rolling

• Re-rolling

• Finishing

• Inspection and Testing

http://www.tatasteeleurope.com/en/products_and_services/products/long/speciality_steels_and_bar/manufacturing_processes/

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Process Route

£15M project to commission unit at Stocksbridge in early 2015

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Continuous Heat Treatment Line

“Available”Stock

No 7HardeningFurnace

Quench

FormingBed 1

TemperF8

TemperF9

TemperF10

FormingBed 2

Saw

Brinell

Exit

If Forming Bed 1 Occupied

Conveyor

Out1

Out2

Extra Temper F10

Downstream Brinell & Saw

19 Pitch Walking Cooling Bank

• Heating to 800°C or 880°C in Continuous Furnace

• In-Line Quencher to harden in homogeneous manner

• Tempering at sub-critical temperature (450°C – 650°C) to soften/modify

• Cool to ambient for Brinell Hardness Testing (approx 5 hours)

• Saw to remove ends and cut required test pieces (3 – 5 cuts per bar)

Enhancements

Cooling time required dependent on bar diameter

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Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘A’

Furnace 10 relatively well utilised for this scenario but some spare capacity evident.

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Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘206’

Matching of Hardening Furnace to Original Design with 2 Temper Furnaces is very good when charges are ‘filled’ to max

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F3

F2

F1

SL A

ccess

Batch Furnaces (Note 2 shorter than others)

Water Quench

Charges delivered (and removed) by Sideloader

GantryCrane

Charger

Pit - Charges can be placed here by crane prior to removal from compound ? Also air cooling here ?

“Supply”

Supply Assumptions• Assume scheduled material all available,i.e.• No Overlength Bars• Dot Matrix stamps replaced by Hard Stamps• All necessary ultrasonic testing done• Correct number of bars

CB

2

CB

1

WQ

1

CB

3

TC

1

TC

2

CB

4

CB

5

WQ

2

F4

OQ

1 F5

F6

CB

6

CB

7

Oil Quench

AirCool

Transfer Car‘Temporary store)

No Oil Quenched Charges

No Hot bars; OK ex water Quench

CB also used as ‘parking’ places, e.g. waiting for removal or to start treatment, or pause in non-critical timed treatment

Side Loader access, introduction of charges into compound and removal of some (some ?)

Batch Heat Treatment Process

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CHT & BHT - Scheduling

• Manual Scheduling approach is the norm

• Until recently, process team leaders were responsible for detailed scheduling ‘on the job’

• ‘Hard’ scheduling recently introduced

• Scheduler prepares detailed 24-36 hour schedule each morning from ‘charge’ information on Heat Treatment system

• Considers– Hard rules and constraints, e.g. next slide– Prioritising ‘late’ or ‘current’ orders – Customer focus– Minimising step changes in temperature between charges – Energy focus– Maximising Utilisation – Throughput focus– ‘Availability’ of material – Commercial interventions and requests

• In general, heat treatment is not the final process so some flexibility

• 12-24 hours published to the Heat Treatment System so material can be assembled and delivered

• Usually, refreshing and/or repairing of schedules only done once per day.

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1. Some orders must temper within x hours (rare)

2. Standard practice is to allocate some furnaces to hardening and some to tempering (because of temperature setting differences) – choice driven by job sheets and experience

3. Aerospace material – tempering restricted to furnaces F1 and F2 or F5 and F6

4. For high temperature hardening charges (e.g. 1000+), use ‘suitable’ furnace next to quench station (if possible)

5. F1 – only furnace which can be controlled to 450°C for low temperature quenching

6. F2 – Can be controlled accurately at temperatures down to 500°C

7. F3, F4 & F5 tend to favour for higher hardening temperatures

8. Can harden in any furnace however

9. F6 ‘always’ reserved for tempering

10. Some charges specify quench method, others give option but times are specified.

11. Time from hardening to quenching should be ‘as quickly as possible’

12. If a furnace is empty it is set to ‘minimum fire’.

13. Furnace temperature losses and ramp rates need to be accounted for.

14. Example, if a furnace was used to harden at 850°C and then is used for tempering at 650°C, would take 2 to 3 hours to cool sufficiently. Generally consider a cooling rate of R°C per minute applies.

15. During removal of charges from a furnace, the control is set to manual whilst thermocouples are removed.

16. Ramp up rate is based on is based on volume of steel in furnace.

17. See next slide for further consideration of heating and cooling rates.

BHT - Examples of Rules and ConstraintsConfirmed/Determined through Trials (Experiments), Experience and Measurements

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°C

MinutesY mins X mins

Regression modelling based on • charge weight• Surface area• Volume• Time since last use• Temperature last use• Last temp – This temp

Charge weight most influential, note that the data incorporates a lot of unrecorded actions by operators.

A lot of scatter

Best result so far shown right. Up to 10 hours red line is mean of ramp up hours for ranges of batch weight shown. Afterwards no evidence to suggest other than flat 4.25 hours should be possible.

Red Line Hours = 0.325*ChargeWeight + 1

From the data however it does seem feasible that this could be accelerated, maybe taking the bottom 25 percentile as the envelope…

BHT – Calculating Required Ramp Up Time

Y – Ramp Up TimeX – Soak Time

FurnaceTemp

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• TSB Research Project led by Preactor International

• To Develop an Autonomous Systems Development Tool (ASDT)

• To Assess the effectiveness of Autonomous Scheduling using end users in real applications

• Project due to be completed in February 2015

• Collaborators

• DeMontfort University

• C4FF - Centre for Factories of the Future

• Tata Steel (end user)

• Plessey Semiconductors (end user)

• TDK-Lambda (end user)

Autoplan – Advanced scheduling algorithms to autonomously produce and update schedules with minimal human intervention

http://www.preactor.com/Home.aspx

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• Looking at different ways of production scheduling that:-

─ Produces schedules autonomously without the need for manual input

─ Produces schedules more often and at fixed times (minutes, hours)

─ Uses different scheduling rules and objectives, compares current status to performance measures and selects the best rule for the next scheduling run

• Using three companies in different industries to develop the tools and assess the benefits and effectiveness of autonomous scheduling

• Also looking at the capability of Genetic Algorithms (GAs) in scheduling applications (DMU and C4FF)

Autoplan – What is it?

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Case Studies – Proof of Concept

Three collaborators/plants have been selected to provide a proof of concept of autonomous scheduling.

Plessey SemiconductorsPlessey Semiconductors

Scheduling of 6” wafer fab production.

Plymouth – Wafer Fabrication

TDK-LambdaTDK-Lambda

Scheduling of PCB component insertion lines.

Ilfracombe – Power SuppliesTata SteelTata Steel

Scheduling of heat treatment furnaces

Sheffield, Steel Bars

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Process Route

£15M project to commission unit at Stocksbridge in early 2015

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Autonomous Agent Approach

Imp

ort

an

ce

Tolerance

C

U

E

Objectives Performance

C – CustomerE – EnergyU - Utilisation

• Independent rule sets are associated with each objective

• Positioning on grid determines which objectives dominate and when to act

• Moving look ahead window evaluates impact of current rule at each ‘event’ and changes rule if tolerances are exceeded

• Multiple iterations may be required to test alternative choices and seek better solutions

Currently evaluating different strategies for Application

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Consider CHT and BHT as Combined System

F8 F9 F10

F7

QuenchBrinell Saw

Sideloader from BHT(Cooled Charges)

Sideloader to Cooling Beds

BUFFER

ReBundle

F8 and F9 used normally, F10 used if system blocked or formed charge for F8/F9 would have to wait more than (30) minutes

Charges cool using linear rule, processed as soon as reach end of prescribed cooling time. Insert BHT (cooled) charge in gap of more than 60 minutes available

Straight Through Option ?(Remove from Process)

BHT Tempers

inserted to F10 if free

and forming bed on F8/F9

free

Sideloader Movements

Middle Crane Movements

End (Magnet) Crane Movements

‘Mark and test’ time per bar before transfer

What is throughput potential of the line for variety of typical CHT Schedules ?

Utilisations and potential bottlenecks ?

Impact on BHT Scheduling ?

Crane removes bars one at a time, between 1.5 and 2.5 mins per cycle

Supply as per Heat Treatment System

Supply

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Run Furnace Without Gaps and Use F10 to avoid blocking – example Schedule ‘206’

Matching of Hardening Furnace to Original Design with 2 Temper Furnaces is very good when charges are ‘filled’ to max

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Run Furnace Without Gaps and Use F10 to avoid blocking and BHT tempering when free + Downstream processing of BHT charges when gaps available. –Schedule ‘206’

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Deployment

InteractiveWorkshops

KnowledgeManagement

DataMining

Rules, Relationships& Patterns

Real-Time Autonomous Scheduling

Process Control

Pathway to Intelligent Manufacturing

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Summary

Through Process Integration – a key concept to keep in mind• Knowledge engineering – understand, secure, deploy• Data Mining > Analytics• Discrete Event Simulation

Growth Curves for Data Integration and Exploitation• Store Everything – Cheaply – (on one platform ?)• Enable Access – Analysis of Anything (by anyone ?)• Distill on Demand – Concept of a ‘data ecosystem’

Establish Frameworks and Tools to Support Collaboration• Attention to Knowledge Management• “From now on we know it”

Visualisation and Visibility key aspects• Deliver Shared Insight and Confidence• Supportive of culture open to automation and process re-engineering

Develop/Identify Options for Real-Time Deployment