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Machine Learning Applications for Furnace Control and Process Optimizationin SteelmakingFuture Steel Forum, Budapest

September 26th, 2019

Giovanni BavestrelliDigital Engineering Director

2019

Techint Group

Tenova Company Profile 2

OUR GROUP

At December 31, 2018

Techint

Humanitas

Tenova

Tenaris Ternium Tecpetrol

4%

5%

5%47%32%

SIX MAIN COMPANIES WITH OPERATIONS WORLDWIDE

6%

Annual RevenuesUS $23.5 billion

Permanent Employees57,100

Total Employees74,500

Continents5 Other

1%

Tenova Company

Tenova Company Profile 3

OUR GLOBAL PRESENCE

Countries

Locations

1940

Employees2500+

30 September 2019Tenova S.p.A. | Confidential & Proprietary 4

Technologies Driving the Digital EraCHALLENGES OR OPPORTUNITIES?

Big Data

Blockchain

Internet of Things

MobilityAugmented

Reality

AutonomousSystems

CloudPlatforms

Data Analytics

Data DrivenDecisions

Cognitive Services

Machine Learning

EdgeComputing

Robotics

Smart Sensors

Digital Twins

Additive Manufacturing

Cyber Security

Collaborative RobotsArtificial

Intelligence

3D Machine Vision

Drones

Wireless Connectivity

Virtual Reality Industry 4.0


Tenova DigitalNEW TEAM TO FACE NEW CHALLENGES

To identify, promote and facilitate a coordinated implementation of new services and business opportunities in Tenova as they are made availableby emerging digital technologies

To solidify Tenova’s position as the leading provider of products and services in the Metals and Mining Industries, making Tenova the idealpartner in a fast changing technological world

Project Product Service Partnership


Partnership with MicrosoftTECHNOLOGICAL PARTNER WITH STRONG INDUSTRIAL BACKGROUND

HoloLens

Cortana Intelligence Suite and Cognitive

Services

Azure IoT Suite

Manufacturing solutions

Microsoft 3D Tools and Windows 10 IoT Core

Vertical integration | Horizontal integration | End-to-end engineering

Industry 4.0 Design Principles

Machine Learning, Stream

Analytics, and Edge analytics

CRMSRMERP…

Process interoperability (IT)Information

Flow (OT)

Collaborative equipment Ecosystem

Interaction

Sourcing: Microsoft


Pilot Digital ProjectsOUR FIRST STEPS IN DIGITAL TRANSFORMATION

\

ROBOTICS

PREDICTIVE MAINTENANCE PLANT OPTIMIZATION

ADDITIVE MANUFACTURING

REMOTE ASSISTANCE

DIGITAL CATALOGS

1 2 3

4 5 6


Our Connected Products in the FieldOUR «GOLD MINE»

Submerged Arc Furnace (SAF)

50Electric Arc

Furnace (EAF)

400Heat treatment

Furnaces

7.000Direct Reduction

Plants

35Burners on Reheating Furnaces

20.000

300 Strip Processing &

Cold rolling mills900 Roll

Grinders


If data is the new oil…

…information is the new energy

…and decisions are the new end goal


IoT ArchitectureCLOUD PLATFORM

IoT Hub

Field Gateway

Customer Plant Microsoft Azure

Stream Analytics

Cold Path

Azure Tables API

Device & Command

Registry

Command Feedback

Pump

Device & Command

API

Tenova WIDE

Future Machines

Current Machines

Event Hub

WebJob

Long TermBlob Storage

Stream Analytics Azure Tables API

Warm Path

Raw data

AdministrationPortal

Hot Path

Azure Active Directory

ConfigurationAPI API

Azure ML ServerData Factory Data Lake

ML Service

Rules engine


Machine Learning SimplifiedA «NEW» PARADIGM FOR SOFTWARE

Traditional Software

Machine Learning

Data

Rules(program)

Data

Results

Results

Rules(model)


Predicting NOx Emissions with Neural NetworksWALKING BEAM FURNACE, 2008

Walking Beam FurnaceTKS Duisburg


Predicting NOx Emissions with Neural NetworksWALKING BEAM FURNACE, 2019

RMSE Train Set 21,6RMSE Test Set 23,4 RMSE Mean 31,5RMSE Median 31,0

MAE Train Set 16,0MAE Test Set 18,3 MAE Mean 25,6MAE Median 25,1


Predicting Steel Temperature in BOF FurnaceBASIC OXYGEN FURNACE

Error on the test set:RMSE of new predictive model: 30,6 degFRMSE of deterministic model: 45.5 degFRMSE Average Improvement: 30%

Data for approximately 4000 heats:Predictions with error >50 degF reduced by 50% to 10%

Static Charge Model for BOF developed by Tenova Canada: Deterministic Mass/Energy Balance model to calculate input materials (HM,SC,..) and blowing Oxygen to reach steel end point Goal to improve final temperature prediction accuracy for better use of fuel/coolant additions

OUTPUT


ORI Martin Lighthouse ProjectORI MARTIN – LIGHTHOUSE PROJECT

Automatic Classification of ScrapCategory from Images

Using Convolutional Neural NetworksTraining on 20000 images, 9 categories


Smart MachineVISION FOR THE FUTURE

Live data (sensors)

Historical Data (Analytics)

PredictiveMaintenance

Self-Optimizing Machines

The Future is Digital


VISION FOR THE FUTURE

«Every Business is a Digital Business»Satya Nadella, CEO Microsoft

«Go Digital or go Home»Dieter Zetsche, ex CEO Daimler AG

Artificial intelligence is a new form of talent, companies won’t be able to compete without it


Digital Transformation DilemmasLESSONS LEARNED

Products versus ServicesIoT & Sensors versus Digital Strategy

Exciting Technology versus Customer ValueReturn on Investment versus Leap of Faith

Cost of Investing versus Cost of Not InvestingPredict and Control versus Learn and Adapt

Internal Digital Team versus Outsourcing Digital ProjectsDigital Team versus Digital Organization

Alone versus PartnershipsBacklog versus Innovation

Share versus Hidehttps://www.linkedin.com/pulse/30-dilemmas-digital-transformation-giovanni-bavestrelli/

https://www.linkedin.com/pulse/30-dilemmas-digital-transformation-giovanni-bavestrelli/


Lifelong learning is here to stayHUMAN LEARNING

www.tenova.com

Thank You!

Giovanni Bavestrelli

[email protected]

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