[For further information about Yandex Data Factory solutions, please contact us at ydf-customer@yandex-team.com Website https://yandexdatafactory.com] Slides for the webinar held on Friday, September 8 “Maximising operational efficiency in process industries with artificial intelligence” Recording on YouTube: https://youtu.be/K16Gxql1gLg Operational efficiency lies at the bottom of every process manufacturer’s success. However, the traditional means of reducing operational costs and maximising the output are usually complicated, lengthy, and incur capital investments. The new artificial intelligence technologies, in comparison, can deliver much-needed efficiencies without significant prior expense and with return on investment measured in months. Technologies similar to the ones helping Amazon and Netflix succeed can now be used to reduce the amount of expensive raw materials, to predict product quality or anticipate defects on the early stages of production, to smoothly adjust equipment parameters in real-time, or to ensure the lowest possible energy use, among other examples. During the webinar Yandex Data Factory’s COO Alexander Khaytin will explain what makes it possible for this technology to allow for such savings and how to use it to your advantage to stay competitive on the market. The webinar will be of interest to the metals industry, oil and gas, chemicals and food processing, as well as costumer goods and the energy sector. It will include examples of AI use in the mentioned industries and recommendations on how to start a profitable AI project. Webinar program ✓ Why process industries are best positioned to profit through the use of artificial intelligence ✓ How AI differs from statistical and physical models already used in process industries ✓ How AI complements and delivers value on top of existing methods ✓ Real AI cases studies in the process industries (YDF’s experience) ✓ Other AI applications for process industries (industry experience) ✓ How it is different from other IT projects: data needed, project life-cycle, etc. ✓ The best processes to optimise with AI for economic and strategic reasons ✓ How to get started with AI to achieve a fast ROI ✓ Q&A

1. Maximising operational efficiency
in process industries with artificial
intelligence
│ 8 September 2017

2. Artificial intelligence (AI)
and process industries:
a perfect match
- Stiff processes
- Big data
- The culture of
experimentation
- “A little optimisation”
means a lot of money

3. From data to value
Provide knowledge
for decision support
Knowledge Decisions
Make operational decisions
automatically
Data Execution

4. How AI and ML differ from models of
physical processes traditionally used
in process industries

5. Processes relying on traditional physical models

6. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters

7. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters

8. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters
Traditional models
of physical
processes
embedded in
process control
systems

9. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters
Traditional models
of physical
processes
embedded in
process control
systems
Expert
judgement

10. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters
Traditional models
of physical
processes
embedded in
process control
systems
Expert
judgement
L(z)
0 z

11. Processes relying on traditional physical models
Results of
chemical
analyses
Equipment
telemetry
Process
parameters
L(z)
0 z

12. Does AI replace traditional models?
No, AI doesn’t abolish traditional models.
It complements them and increases their accuracy.

13. 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
?
?
?
?
?

14. 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

15. Use cases in process industries

16. │ Optimising ferroalloy use
│ during steel production
Saving
expensive
ingredients

17. Here comes the optimisation
$$$$$$
Optimisation potential
$$$
Cost savings achieved

18. Smelting model. Three-steps modeling
Simple (e.g. linear) dependency on
the most important features 𝑧⃗ :
𝑧⃗ - Values of technical parameters
𝑦%
- Target (mass percent of
chemical element k)
𝑧⃗&, 𝑦%
- Historical dataset
𝑦%
≈ L(𝑧⃗)
More sophisticated dependency
on the whole set of features 𝑥⃗:
𝑦%
≈ F 𝑥⃗ =L 𝑧⃗ + M(𝑥⃗)
Probabilistic final model:
1 2 3
Smelting
model
Y D F
Probability
Amount of Mn
Permitted
chemical
range
L(z)
0 z 0

19. Optimisation
The domain of confident
meeting the specifications
Threshold of confidence for
meeting the steel
specifications
Dopant2,kg
Dopant 1, kg
In a certain way it corresponds to the
range of the restrictions.

20. │5% of ferroalloy
│costs reduction
│>$4m a year in
projected savings
Magnitogorsk
Iron & Steel
Works

21. Optimisation of raw material use: other cases
Animal feed production Chocolate production Gold extraction

22. Optimisation of animal feed production
— Complex technological process
managed by an operator
— Strict requirements on chemical
composition and amount of moisture
content
— Goals:
〉To optimise the consumption of
raw material, electricity, gas,
water, gas, etc.
〉To decrease the variability of the
process

23. Animal feed production process
Raw materials
measurements
Milling Preconditioning Extrusion Drying
Process data
Spraying Cooling
Extruder operator Dryer operator
Final product
measurements
Server

24. Optimisation of gold extraction process
〉20-40% is the share of cyanide
costs in ore processing
〉To define the optimal amounts
of cyanide to be added and its
concentration
〉In order to decrease overall
cyanide costs while maintaining
the levels of gold recovery

25. Optimisation of chocolate conching process
〉A lot of uncertainties in the
process and fluctuations in
quality of raw materials
〉To recommend the optimal
amount of cocoa butter to be
added
〉In order to decrease the
consumption of cocoa butter
while keeping up with final
product quality

26. │ Timely reaction for
│ optimal decisions
Quality
prediction

27. Determining optimal production routes
Route 1
Route 2
Rules based
on statistics/
guidelines
Action
choice
Production
process

28. Determining optimal production routes
Predictions
Production
process
Predictions
Route 1
Route 2
Action
choice

29. │Analysed data on
│17,000 slabs
│48% of defect slabs
│predicted in first
│10% of all slabs
Slab quality
prediction

30. │ It’s hard to manage manually
│ with precision due to a
│ multitude of factors that
│ change dynamically
Optimisation
of process
parameters

31. Optimisation of moisture content in tobacco
〉Use of different additives,
fluctuations in raw materials and
time gap after drying affect the
outcome
〉Goal: to predict required
moisture levels in order to
manage speed and temperature
of the drying machine
〉Result: 44% decrease in the
average error as compared to
existing model

32. Optimisation of diffusion process
〉A certain portion of sugar is lost
during its extraction from sliced
sugar beets
〉Its amount depends on the
operational parameters of the
diffuser unit and the ability to
adjust them on time
〉Goal: to increase throughput
(sugar recovery) of diffuser unit

33. Optimisation of gas fractionation
〉Some parameters should be
adjusted before the chemical
composition of stream is known
〉Changing the operating mode
too fast may lead to disruptions
〉Some mistakes of raw
processing cannot be fixed later
〉Goal: to improve energy
efficiency while maintaining
high throughput

34. How AI is used by other process manufacturers
Production efficiency
optimisation: Hershey
saved $500,000 (on
one machine)
Anomaly detection in
beer fermentation
process: Deschutes
Brewery Inc.
Automatic classification
of nutritional deficiencies
in coffee plant (using
computer vision)

35. How AI is used by other process manufacturers
〉Calving prediction from activity, lying, and
ruminating behaviors in dairy cattle
〉Prediction of insemination outcomes in
Holstein dairy cattle
Other cases in dairy production:

36. Practical issues of AI implementation

37. Level 2 Process Control
(DCS / SCADA / APC)
How AI solutions are integrated
Operator interface
Control
execution
(Level 1)
Production
process
Sensors, real-time process data
Existing process control environment
Controlled KPIs
Manipulated variables,
commands

38. Level 2 Process Control
(DCS / SCADA / APC)
How AI solutions are integrated
Operator interface
Control
execution
(Level 1)
Production
process
Sensors, real-time process data
Existing process control environment
Controlled KPIs
Manipulated variables,
commands
AI-based model
(no interface)
Prescriptions
Recommendations
Model KPIs

39. 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

40. 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 +
.

41. Questions?
yandexdatafactory.com
ydf-customer@yandex-team.com
Contact us at