SMART INDUSTRIAL ASSETS OPTIMIZATION AND MANAGEMENT PLATFORM
Production business efficiency increase via artificial intelligence and digital modelling
What causes loss of profit for industrial companies?
Emergency shutdowns
Increased maintenance cost due to late defects recognition or unplanned downtime
Inefficient operating modes
Overconsumption of fuel and materials or poor perfomance
Low product quality
No system recommendations for product quality improvement
What we know about industrial equipment
Operational data
Physics of production processes
Money from production and operational costs
How we use this knowledge to save money
1
Equipment "health" monitoring
2
Connection of production and business efficiency
3
Deep equipment modes and malfunctions analysis
We connect all the knowledge about equipment on the Platform
CYBERPHYSICS PLATFORM
Equipment defects?
We will predict them!
Loss of money on inefficient modes?
We will optimize them!
High operation costs?
We will reduce them!
CYBERPHYSICS platform functionality
Optimization
Of production modes for efficiency increase
Smart changes planning
"What-if" analysis allows to estimate the expediency of equipment settings changes and new equipment integration
Forecast
Of malfunctions that have never happened, residual resource real-time calculation
Recommendations
For equipment settings, repair and replacement planning — findinging the most business efficient variant
Core technology
Platform CYBERPHYSICS
IIoT/SCADA data from sensors
Digital models of equipment and processes
Machine learning of operational data and synthetic data from digital models
Digital twins of equipment and processes
Online and prescriptive malfunctions analysis
Modes optimization
"What-if" analysis for equipment settings
What-if analysis for changes planning: how it works
Using of equipment digital models allows to analyze any equipment settings changes before make them
Modes optimization
What if we change the fuel consumption and reset the air filtration parameters?
?
Equipment replacement
What if we replace the pump by the new one?
?
Increase the second compressor fuel consumption by 15%
Replace the pump in 2 months
Our advantage is our core technology
Lack of sensors data is not a problem
The platform can be implemented even if IIoT/SCADA system is recently integrated
Forecast of any possible defects
Our technology finds out the real reason of malfunction by deep analysis of equipment "physics"
What-if analysis for changes planning
The environment for preliminary changes modelling and analysis
Place in company IT-landscape
ERP/BI
Platform CYBERPHYSICS
Hybrid models
Digital models
Real Data
APCS
SCADA/IIoT system
Production processes data
Production efficiency metrics
Equipment modes
Sensors data
Recommendations
To change modes and settings
Recommendations
To plan the repair, service and replacement
METALLURGY
Out-of-furnace processing
Continious casting machine
Rolling production
Saving
$75 million
per year
$75 million
per year
+1
-40%
-20%
+30%
Production quality improvement
Steel quality grade
Improvement of equipment condition
Decline of lining wear
Operations optimisation
Reduced downtime from rolls breaks
Working mode optimisation
Increase of production output
OIL & GAS INDUSTRY
Saving
100K RUB
per day for 1 department
100K RUB
per day for 1 department
Optimisation of operating costs
Reduced fuel gas consumption
Technical condition analysis and diagnostics
On the plant of gas pumping equipment
Modeling error less than
Physical and mathematical models of gas turbines
Technical condition forecast
Before fatal development of a defect
Air cleaning system
Gas and air duct
Oil system
2%
4%
400 hours
10
units
units
MINING & MINERAL PROCESSING
Saving up to
4 million RUB
from an emergency shutdown
4 million RUB
from an emergency shutdown
Key technology
Optimisation and cost reduction by
Downtime prevention and identification of the causes of malfunctions
Technical condition analysis results
Malfunctions prediction and recommendations for their elimination
Technical condition
index forecast
index forecast
Reducing the likelihood of industrial accidents
Hybrid equipment
model
model
Vibration analysis and
predictive models
predictive models
Key
tech
tech
In 1
week
week
Real-time
80%
AUTOMOTIVE INDUSTRY
Saving
€500k
€500k
Optimisation of injection molding of plastic parts
Precise recommendations in order to achieve effective performance and reduce risks
Pre-training the digital model on
Powered by deep learning machine intelligence technology
Optimised injector placement
Knowledge of the physics of the plastic spreading process and the customer's expertise
Accelerated prediction of the optimal casting mode
Choice of operating mode in
2 seconds instead of 20 days
2 seconds instead of 20 days
2 sec
200
pieces
pieces
2 components
Balance
Clients production IT-infrastructure requirements
Available IIoT/SCADA system (even recently integrated)
Steps to implement the Platform
Development of equipment digital models
Simulation and analysis of equipment production physical processes
Connecting the Platform to IIoT/SCADA system
Complement of digital models with real operational data from sensors
Validation of digital models
Modelling of all the possible malfunctions and defects
Real-time recommendations
Malfunctions monitoring and forecast, optimal modes selection, real-time residual resource calculation, repair and replacement smart planning
Steps to implement the Platform
Development of equipment digital models
Simulation and analysis of equipment production physical processes
Connecting the Platform to IIoT/SCADA system
Complement of digital models with real operational data from sensors
Validation of digital models
Modelling of all the possible malfunctions and defects
Real-time recommendations
Malfunctions monitoring and forecast, optimal modes selection, real-time residual resource calculation, repair and replacement smart planning
Types of service
Local project
Cyberphysics core technologies implementation for local optimization solutions
1-3 months
Pilot project
Integration of Cybephysics platform to IT-infrastructure
2-6 months
Scaling
Extending the use of an already connected platform to new equipment
CYBERPHYSICS news
About Cyberphysics company
The company if founded on the research base of Skolkovo Institute of Science and Technology, Cyber-Physics System Laboratory
Skolkovo resident
The team includes specialists from oil & gas, metallurgy, aerospace, artificial intelligence, alumni of Russian top technical universities (BMSTU, MIPT, MAI)
Cyberphysics Management Team
Sergei Nikolaev
CEO
Sergei Belov
CTO
Alexander Akhmedov
COO
Mikhail Gusev
CBDO
Board of Directors
Sergei Nikolaev
Sergei Belov
Mikhail Gusev
Ighor Uzhinsky
Viktor Chervyakov
Representative of the Skolkovo Ventures investment fund
Fabio Cacciatori
Strategic Development and Sales
Non-Voting Member of the Board of Directors
Non-Voting Member of the Board of Directors
Tell us about your production problems!
Fill in the form and we will contact you
+7 926 257 23 57
info@cyberphysics.xyz
info@cyberphysics.xyz
Moscow, Skolkovo
Bolshoi Boulevard street, 30/1
Bolshoi Boulevard street, 30/1
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