CYBERPHYSICS
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Predictive analytics of equipment - Production processes optimization
- Agent-based simulation of production systems
Who we work for:
Oil&gas, Energy, Chemicals, Metallurgy, Machinery
Tasks to be solved:
Predictive analytics of equipment
Optimization of technological modes
Why us:
№1 by number of predictive analytics implementations in the Russian Federation
ML + engineering knowledge
Templates for most spread equipment
Our Clients:
Oil&gas, Energy, Chemicals, Metallurgy, Machinery
Tasks to be solved:
Predictive analytics of equipment
Optimization of technological modes
Why us:
№1 by number of predictive analytics implementations in the Russian Federation
ML + engineering knowledge
Templates for most spread equipment
Our Clients:
Cases
- Oil&Gas
Predictive analytics of gas turbines47% prevented emergency stops
$2,5M/year economic effects
- Metallurgy
Optimization of gas consumption in furnaces10% gas consumption savings
$560 000/year for a complex of furnaces
1,5% reduction in the
cost of pig iron production
- Energy
Predictive analytics of PowerPlants$1,5M per 1 powerplant per year
10 powerplants full scale
implementation
> 80 emergency situations prevented
- Mining and Processing Plants
Predictive diagnostics of equipment technical condition at the factory$560 000 savings from preventing one emergency shutdown
3000 tons of production preserved by preventing emergency shutdowns
- Chemistry
Optimization of the chemical production shop's operations30% reduction in product cost
3-fold increase in line efficiency achieved without deep modernization.
How predictive analytics works
Data Analysis and Model Development
Data Analysis. Historical equipment operation data
Training of a Hybrid Model. Based on field data and equipment physics
Defect classifier setup. Identification of possible defects
Data Analysis. Historical equipment operation data
Training of a Hybrid Model. Based on field data and equipment physics
Defect classifier setup. Identification of possible defects
Equipment Monitoring
Fault Detection and Elimination
Detection and classification of defect type
Demonstration of defect and warning
Early-stage defect elimination without losses or downtime
Detection and classification of defect type
Demonstration of defect and warning
Early-stage defect elimination without losses or downtime
Product and functionality
CyberStudio software platform for predictive equipment analytics and optimization of technological processes. Enables the creation of models for:
- Equipment
- Processes
- Equipment
- Processes
Functionality
- For process optimization, recommendations are provided for optimal control parameters.
- Model Creation. Allows for creating models or using templates, fine-tuning models, and evaluating their quality.
- Defect classifier setup. Allows for populating the database with equipment defect information
- Optimizer mode setup. Enables real-time analysis of optimal control parameter values
- Monitoring:
- For process optimization, recommendations are provided for optimal control parameters.
Predictive monitoring of production assets
Hierarchy of IT solutions in the enterprise
Business process of equipment predictive monitoring
Data collection, storage, and analytics in CyberStudio
Creation of an enterprise information mode
Notification of a detected defect
What awaits us tomorrow?
Integration of Decision Support Systems with LLM
Integration of Decision Support Systems with LLM
Data analytics + ML + LLM
Integration of Predictive Analytics and Large Language Models (LLM) for Automatic Interpretation and Generation of Recommendations for Operators.
"Understanding" of Equipment
Utilization of all documentation to improve the accuracy of recommendations. The model is integrated into the enterprise’s operational loop.
Efficiency and Savings
It is sufficient to have 2-3 engineers to analyze the operation of hundreds of equipment units 24/7, prevent accidents, and transition to condition-based maintenance.
Continuous learning
The LLM uses all situations as context for the continuous improvement of the enterprise’s knowledge base.
Integration of Predictive Analytics and Large Language Models (LLM) for Automatic Interpretation and Generation of Recommendations for Operators.
"Understanding" of Equipment
Utilization of all documentation to improve the accuracy of recommendations. The model is integrated into the enterprise’s operational loop.
Efficiency and Savings
It is sufficient to have 2-3 engineers to analyze the operation of hundreds of equipment units 24/7, prevent accidents, and transition to condition-based maintenance.
Continuous learning
The LLM uses all situations as context for the continuous improvement of the enterprise’s knowledge base.
How LLM Works in СyberStudio
Integration with LLM
Examples of Virtual Cyber-Engineer Implementation
Notification of a detected defect (without a Cyber engineer)
Using a Cyber-engineer for clarifying recommendations
LLM Agent — Development plans
Контакты:
Sergey Nikolaev
CEO
+7 926 257-23-57
s.nikolaev@cyberphysics.xyz
cyberphysics.xyz
Sergey Nikolaev
CEO
+7 926 257-23-57
s.nikolaev@cyberphysics.xyz
cyberphysics.xyz
