cutting steelmaking costs without sacrificing quality. machine learning for metallurgy
TRANSCRIPT
Cutting Steelmaking Costs Without Sacrificing Quality
Machine Learning for Metallurgy
Who We AreYandex 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
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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
Yandex Data FactoryFounded 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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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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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 yours6
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.7
What is machine learning?
│Machine learning algorithms can learn from data and make predictions on new data
How traditional analytics works
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Human
Gender
Age
Income
Balance
Trend3M
RestaurantSpend
TravelSpend Hypotheses
BestHypotheses Results
How machine learning works
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x100
Machine learning
x10100
Hypotheses
x1000
Best hypotheses
Results
Service Concept
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› 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.
Client CaseTaskTo 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
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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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Service Concept
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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.
Service Concept
16Smelting model
Production parameters
Steel specifications
Recommendations
FeSiMn17 : 442.5FeMn78 : 1652.2Ni : 1158.2
OptimisationTarget function and constraints
Confidence Cost
Service is based on the historical data on previous smeltings, and is unique for the specific plant and equipment.
Service Concept
17Smelting model
Production parameters
Steel specifications
Recommendations
FeSiMn17 : 442.5FeMn78 : 1652.2Ni : 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
Service Concept
18Smelting model
Production parameters
Steel specifications
Recommendations
FeSiMn17 : 442.5FeMn78 : 1652.2Ni : 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
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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
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Chemical analysis
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?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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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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How to get started? Project planStage 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 +
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.
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?
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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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
