For further information about Yandex Data Factory solutions, please contact us at ydf-customer@yandex-team.com Metallurgy companies must balance two competing demands: keeping production costs to a minimum while still ensuring that the resulting steel composition complies with all requirements. Given how difficult this balance is to strike, you might find it hard to believe that metallurgy companies can actually achieve 5% cost optimisation with no investments in expensive equipment and software. But that is exactly what Magnitogorsk Iron and Steel Works managed to do with the help of Yandex Data Factory’s machine learning technology. You’ll learn how implementing Yandex Data Factory’s ferroalloy optimisation service has resulted in projected savings of more than $4m a year for Magnitogorsk Iron and Steel Works. We also discuss other advantages these new technologies bring to metallurgy and give practical advice on how to get started with your first machine learning and big data analytics project so that your company can also cut costs while maintaining the same high quality of resultant steel.

1. Cutting Steelmaking Costs
Without Sacrificing Quality
Machine Learning for Metallurgy

2. Who We Are
Yandex Data Factory is an international B2B branch of Yandex, the leading Russian search engine, focused on
Machine Learning & Big Data technologies and solutions for businesses
3
The Largest
European Internet
Business
is
› 2011 IPO on NASDAQ ($7.19B capitalization)
› $820 MM Revenues in 2015
› More than 6000 people
(2500+ of those are engineers)
› Proprietary Machine Learning Algorithm
MatrixNet
› Computer vision & image recognition
› Collaboration with CERN
› 211,000,000 search queries processed
daily
› Tens of thousands of servers in Finland,
Netherlands, Russia
Company that built
its success on
technologies
Big data company by
nature

3. Yandex Data Factory
Founded in 2014, Yandex Data Factory provides machine learning solutions to clients across
various industries, from online and retail to healthcare and manufacturing.
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4. 1/3
Alexander Khaytin, YDF’s Chief Operating Officer
▌ Current state of the industry: where and how will the next industrial
revolution take place?
▌ What value can machine learning and big data analytics bring to steel
production?
▌ How we help Magnitogorsk Iron and Steel Works save over $4m
annually on ferroalloy optimisation. Real case study
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5. Where and how will the next industrial
revolution take place?
› Innovations problem: results are great, but someone has to be the
first, and it’s not an easy way
› Industry problem: everything is optimised, the processes are rigid,
competitors do it the same way
▌ How to be the first to succeed, not being the pathfinder? How to pull
through the industry conservatism?
▌ Look into the industry next to yours
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6. Example of a cross-industry innovation
▌ Liberty Ships by Kaiser Shipyards
› Mass production of ships couldn’t be delivered with the use of existing
technologies (estimated time: 6 months)
› Revolutionary new way of shipbuilding (assembling mass produced parts) came
from a person who never built a ship before.
› “The unfamiliarity of Kaiser and others with ship building was undoubtedly a
factor in their success at developing an innovative construction system”.
[Sawyer and Mitchell]
› Liberty ships were typically produced in 42 days, and one in less than 5 days.
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7. What is machine learning?

8. │Machine learning algorithms can
learn from data and make
predictions on new data

9. How traditional analytics works
10
Human
Gender
Age
Income
Balance
Trend3M
RestaurantSpend
TravelSpend Hypotheses
Best
Hypotheses Results

10. How machine learning works
11
x100
Machine
learning
x10100
Hypotheses
x1000
Best hypotheses
Results

11. Service Concept
12
› Metallurgy companies must balance two competing demands: keeping the use of
costly chemicals – ferroalloys – to a minimum during production, and making sure
that the resulting chemical composition complies with all requirements.
› In order to keep expenditures down, it is essential to know which chemicals to use
and in what quantities.
› Uncertainties in the steelmaking process, however, make this task quite
complicated. The chemical composition of steel can not be pinpointed even if the
exact amount of each ferroalloy is known and a host of other parameters are set.
To meet this challenge, Yandex Data Factory (YDF) developed a service that applies advanced
mathematics (machine learning technologies) to historical data on previous smeltings. The service
makes it possible to predict production outcomes with the highest possible degree of accuracy
and thus optimise the parameters to decrease the costs.

12. Client Case
Task
To reduce the usage of ferroalloys in an oxygen-converter plant
while complying with quality requirements
Data used (more than 200,000 smeltings over 7 years):
- Mass of scrap and crude iron
- Steel grades specifications
- Technical parameters of the oxygen-conversion stage
- Technical parameters of the refining stage
- Results of chemical analyses
- References for steel grades, ferroalloys and other additives
- Chemical composition requirements and standards for ferroalloy use
Results
- A service that recommends the optimal consumption of ferroalloys
and other materials at a given stage of the production process
- Service integrated in the existing customer software
- Reduced consumption of ferroalloys (average of 5%)
Optimization of ferroalloy
consumption for Magnitogorsk
Iron & Steel Works
5%
> $4myearly economic effect
average decrease of ferroalloy
consumption

13. 2/3
Victor Lobachev, modelling and optimisation expert, YDF Research Team
▌ Ferroalloy optimisation solution: how it works and integrates into
existing systems
▌ What data does the machine learning service for ferroalloy
optimisation need in order to work? How much data is enough to get
results?
▌ How machine learning is different from experience-based
approaches traditionally used during the steelmaking process
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14. Service Concept
15
With current production process, the resulting amounts
often fall in the middle of the range. While complying
with standards, it means that some efficiency gains are
possible.
The requirements for the specific steel grade list a specified range for the amounts of each chemical element in
the final mix.
Using advanced mathematical modelling, we predict the
exact amounts of ferroalloys to be added and thus
consistently get closer to the lower boundaries. While
still complying with the quality requirements, it helps
decrease the actual production costs.

15. Service Concept
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Smelting model
Production
parameters
Steel
specifications
Recommendations
FeSiMn17 : 442.5
FeMn78 : 1652.2
Ni : 1158.2
Optimisation
Target function and
constraints
Confidence Cost
Service is based on the historical data on previous smeltings, and is unique for the specific plant and equipment.

16. Service Concept
17
Smelting model
Production
parameters
Steel
specifications
Recommendations
FeSiMn17 : 442.5
FeMn78 : 1652.2
Ni : 1158.2
OptimisationGoal and constraints
Confidence Cost
Service is based on the historical data on previous smeltings, and is unique for the specific plant and equipment.
The model predicts the results of
the “virtual smelting” based on any
given combination of parameters

17. Service Concept
18
Smelting model
Production
parameters
Steel
specifications
Recommendations
FeSiMn17 : 442.5
FeMn78 : 1652.2
Ni : 1158.2
OptimisationGoal and constraints
Confidence Cost
Service is based on the historical data on previous smeltings, and is unique for the specific plant and equipment.
The model predicts the results of
the “virtual smelting” with its own
set of parameters
The model optimises the cost of
smelting, still meeting the
requirements

18. 19
CastingConverter stage
The furnace is
charged
Temperature and
oxidation
measured at the
end of blowing
Chemical analysis
I II IV
Recommendations of the converter stage Recommendation at
the refining stage
Refining
stage
Refining
stage
Refining
stage
Timeline
III
Chemical analysis

19. Closer look at the data used
▌ Mass of scrap and crude iron
▌ Steel grades specifications
▌ Technical parameters of the oxygen-conversion stage
▌ Technical parameters of the refining stage
▌ Results of chemical analyses
▌ References for steel grades, ferroalloys and other additives
▌ Chemical composition requirements and standards for ferroalloy use
› How much data is enough?
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20. Service deployment
▌ Can be deployed on client’s premises
▌ Resource-intensive machine learning is performed in Yandex’
datacenters
▌ Integrates with client’s manufacturing execution systems (MES) via
simple REST and other APIs
▌ Access to the service and model upgrades are provided for a yearly
subscription fee
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21. 3/3
Closing thoughts by Alexander Khaytin
▌ How to get started with machine learning to reduce steelmaking
costs
▌ How machine learning gets you a return on your investment within
the very first year
▌ Managing expectations when employing new technology
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22. How to get started? Project plan
Stage Scope Timeframe
Preliminary phase - Confirmation of the details of the technological process (input
- output parameters)
- Data transfer
- Preliminary data analysis
- Preparation of the individual project plan
1 month
Service development &
Integration
- Development and optimisation of the machine learning model
- Service integration with existing customer software
2 months
Pilot - Experimental testing of the service
- Measurement of the economic effect
1 month
Commercial use - Regular support and quality monitoring, including model
quality updates
1 year +

23. Service benefits
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Universally applicable
The service makes recommendations for
different steel grades, including automobile
body sheet, capped steel, rimmed steel, and
other. It can also be extended to modify the
parameters of other production processes.Effect-based pricing
We deliver a service, not software. You
only pay if there are efficiency gains from
the service usage.
Constant quality improvement
The service learns from data on new
smeltings, further increasing the prediction
quality and cost savings.
Same year ROI
Service directly helps to reduce production
costs, and the results are measured in field
experiment.
Easy integration
The service integrates with existing
customer systems. There is no need to
install and integrate new complicated
software.

24. What to expect from big data analytics and
machine learning?
Dos:
Real-time recommendations:
- How much of what alloy to add now
- To get certain result
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Don’ts:
Simple rules, explanations
- Why to add this ferroalloy?
- Should we always add this much?

25. You can also optimise consumption of:
– Fluxes
– Oxygen in the oxygen converter process
– Argon in vacuum degassing units
Such recommendation service can be applied to both oxygen converter and
refining stages of the process.
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26. Additional use cases in manufacturing
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Predictive maintenance
à Early alerts on potential failures based on past
equipment logs and maintenance data
à Complex equipment often combines elements from
different vendors, and the additional value lies in
possibility to deliver a model on the level of the
“whole” system
Computer vision
à Visual quality control of raw materials or
resulting product
à Visual analysis of the production
parameters (e.g. parameters of scrap or
torch in steelmaking)
à Safety monitoring
Quality prediction
à Prediction of the expected product quality at
different stages of the production process
à Early detection of possible defects
Demand prediction
à Highly precise short-term and mid-term
demand prediction for supply chain
optimisation, infrastructure development
planning & scheduling
Demand prediction for spare parts
à Prediction of the expected demand for spare parts
to allow timely ordering and decrease downtime

27. Thank you!
ydf-customer@yandex-team.com
It’s time for your questions.
yandexdatafactory.com