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Artificial Intelligence and Metals
│ Making the technology │ of the future pay off now
AI and metals: a perfect match
- Stiff processes
- Big data
- The culture of experimentation
- “A little optimisation” means a lot of money
From data to value
Provide knowledge for decision support
Knowledge Strategy
Make operational decisionsautomatically
Data Execution
How AI and ML differ from models of physical processes traditionally used in metals
Processes relying on traditional models of physical processes
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Expert judgement
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
Traditional models of physical processes
embedded in process control
systems
Expert judgement
L(z)
0 z
Processes relying on traditional models of physical processes
Results of chemical analyses
Equipment telemetry
Process parameters
L(z)
0 z
So how AI differs from traditional models?
AI doesn’t abolish traditional models.
It complements them and increases their accuracy.
What this AI is good for
Established,repetitive process
Uncertainty in inputs
Well-defined, measurable outcomes
to create value to start quickly to measure success
? ??
??
Checklist for a process to start using AI
〉The process is important and costly
〉The more complex, the better
〉There’s a KPI that can be measured
〉Enough historical data at hand
〉Experimenting is possible
Use cases in the metals industry
Optimisation of Ferroalloy Use
Slab Quality Prediction Blast Furnace Optimisation
│5% of ferroalloy│costs reduction
│>$4m a year in projected savings
Magnitogorsk Iron & Steel Works
How we achieved it
Outcomes
Ferroalloy use
Quality requirements
Uncertainty
Reactions between >20
elements
Scrap metal composition
? ?
Here comes the optimisation
$$$$$$Optimisation potential
$$$Cost savings achieved
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?
│Analysed data on │17,000 slabs
│48% of defect slabs │predicted in first │10% of all slabs
Slab Quality Prediction
Determining optimal production routes
Route 1
Route 2
Rules based on statistics/
guidelines
Action choice
Production process
Determining optimal production routes
PredictionsProduction
process
Predictions
Route 1
Route 2
Action choice
Other use cases in the metals industry
│ Raw material use
〉Optimising consumption of fluxes, oxygen in the oxygen converter process, or argon in vacuum degassing units
〉Decreasing the use of reagents in ore beneficiation
│ Process optimisation
〉Decreasing energy use in electric arc furnace steelmaking through timely parameters adjustment
Other use cases in the metals industry
│ Improved measurements
〉Virtual sensors to estimate the amount of carbon in steel during the oxygen converter process
〉Computer vision to estimate scrap composition
│ And many other.
Why you should use artificial intelligence
No capital investments
No disruption of existing process
3-6 months to implement
Immediate ROI
― Capital investments
― Process redesign
― Lengthy deployment
― ROI in 5-10 years
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 and 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 +
.
Some details on ferroalloy optimisation
Ferroalloy optimisation service concept
Production parameters
Steel specifications
Goal and restrictions
Confidence Cost
Recommendations:FeSiMn17 : 442.5FeMn78 : 1652.2
Ni : 1158.2
Smelting model
Optimisation
The module optimises the cost of smelting, while ensuring compliance
with chemical requirements
The model predicts the results of the “virtual smelting” with
its own set of parameters
Smelting model. Three-steps modeling
Simple (e.g. linear) dependency on the most important features 𝑧 :
𝑧 - Values of technical parameters𝑦%- Target (mass percent of chemical element k)𝑧&, 𝑦% - Historical dataset
𝑦% ≈ L(𝑧)
More sophisticated dependency on the whole set of features �⃗�:
𝑦% ≈ F 𝑥 =L 𝑧 + M(𝑥)
Probabilistic final model:1 2 3
Smelting model
Y D F
Prob
abili
ty
Amount of Mn
Permitted chemicalrange
L(z)
0 z 0
OptimisationThe domain of confident
meeting the specificationsThreshold of confidence for
meeting the steel specifications
Dop
ant 2
, kg
Dopant 1, kg
In a certain way it corresponds to the range of the restrictions.
Timeline
Steelmaking process
Converter stage Refining stage Refining stage Refining stage Casting
The furnace is charged
Temperature and oxidation measured at
the end of blowingChemical analysis
Chemical analysis
I II III IV
Recommendations at the converter stage Recommendations at the refining stage
Q&A
Alexander KhaytinChief Operating Officer
Viktor LobachevResearch Director
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