INTERNET OF THINGS IN STEEL MANUFACTURING Furnace –Burden Distribution Model Basic Oxygen Furnace –Steel Bath & Slag models Models provide accurate predictive

Copyright © 2014 HCL Technologies Limited | www.hcltech.com

INTERNET OF THINGS IN STEEL MANUFACTURING

Computer Applications Technology Committee Meeting,

9th Oct 2014Presented by HCL Technologies

Copyright © 2014 HCL Technologies Limited | www.hcltech.com2

Agenda

Definition1

Significance of IoT2

Making a Steel Plant Smart 3

Modeling – How IoT can help 4

Big Data Analytics – How IoT can help 5

Cloud & SaaS6

IoT & MES7

IoT Framework 8

Challenges in Implementing IoT9


Definition

IoT is enabler for digitizing Manufacturing to be highly efficient and productive. It includes

Using intelligent devices, equipment to produce assets;

a cloud-based infrastructure for data processing

“big data” storage capable of addressing a complex value chain;

a combination of descriptive, predictive and prescriptive analytics and software to support asset and system optimization;

Timelines


Significance of IoT

Smart

Manufacturing is

the targeted

“‘New Normal”

US’s SMLC &

Germany’s

Industry 4.0

Every Major

Vendor offers

Remote

monitoring

services

Automation to

substitute ageing

skilled work force


Making a Steel Plant Smart

Each Machine is like an Individual

Implement Condition monitoring/Predictive Analytics

Example – System & Causes of Hydraulic system

in Hot Rolling Plant

The process automation data is not enough for diagnosing

such complex machinery as a plate rolling mill.



Interoperability among systems & equipment



Plant Operations Management from Mobile Devices

Accessories on mobile phones – Intelligent Data acquisition devices

Mobile Workflow Management

In near Future –Google Glass



Overall, the Ability to log all activities in a plant –either by humans or machines for analysis

Interoperability – Control System, Mobile Devices

Ease of Remote Management

Wireless

RFID

Track & Trace logistics solutions


Modeling – How IoT helps

Many models used in Steel Operations - e.g.

Sinter plant - Ore bed distribution in Sinter Plant

Blast Furnace – Burden Distribution Model

Basic Oxygen Furnace – Steel Bath &

Slag models

Models

provide accurate predictive

information leading to energy savings

IoT Engines

Packages for Genetic Algorithm

Abundant past data for model creation

R can be used for Modeling-solving differential equations – deSolve, ReacTran

CARET package in R –ideal for modeling

Fitment of existing models becomes very easy.


Big Data Analytics – How IoT helps

Dynamic Rescheduling using IoT

Hot Charging

MES do not have vision of upstream units


Big Data Analytics – How IoT helps

Pattern Recognition

Constant Risk monitoring based on past data

Video Analytics on Cloud

Thermal Imaging of refectories,

High-speed video captures roughing mill operation for analysis

Complex Event Processing (CEP)

Data Stream Analysis – e.g. Alert when sinter size exceed 5% deviation in 10

minute timeframe

Microsoft StreamInsight,

SQLStream –open source


Cloud & SaaS

Performance and Availability Guaranteed

Unlimited, almost inexpensive data storage

Multi Tenanted Architecture

Pay only for what is used

Subscription Model – Gold, Silver, Bronze

Services offered

Analytics as a Service

Storage as a Service

Applications as a Service


Cloud & SaaS

Name Services

Microsoft Azure Machine

Learning

Based on Azure platform, MS offers standard R and their research

division developed Algorithms

Yottamine Analyticsprovides machine learning (SVM) based predictive models in the cloud.

Pricing is based on usage.

Zementis, Incoffers predictive models as a fully hosted service through Amazon

Elastic Compute Cloud (EC2).

Sight Machine merges machine vision and big data to control quality.

Ayasdiprovides a mathematical framework for analysis of complex, multi-

variate datasets

SkyTree Successfully deployed algorithms for preventive Maintenance

Select List of Providers of Machine Learning as Service


ML Studio in MS Azure


IoT & MES

MES is specific to 1 plant, IoT is plant wide

Power of IoT is on the Cloud,

Analysis Services, Pay as you Use

Periodic Application change

Remote Monitoring of equipment

Large Historian Data Analysis


IoT Framework


Challenges in IoT Deployment

Cyber security

Can affect any equipment

Apprehensions of Cloud Security

Making old equipment smart

Reliable Internet connection

scalability, security, availability and inexpensive

Changing Managers/Operation Mindset

Training

See Tangible Benefits

Copyright © 2014 HCL Technologies Limited | www.hcltech.com

Q&A

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Documents

INTERNET OF THINGS IN STEEL MANUFACTURING Furnace –Burden Distribution Model Basic Oxygen Furnace –Steel Bath & Slag models Models provide accurate predictive