Ammonia Cracking Plant Digital Twin for Emergency Scenario Response
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Solution Overview
Problem
Conventional methods struggle to manage complex interactions between components in ammonia cracking plants, particularly under emergency conditions, leading to instability and difficulty in ensuring stable operation.
Innovation Solution
A system and method utilizing a digital twin model to simulate and analyze emergency scenarios, incorporating real-time data to predict adjustments, identify vulnerabilities, and generate control scenarios for stable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to manage ammonia cracking plants, then device complexity is reduced, but reliability deteriorates under emergency conditions
Solution Approach 1:
The patent creates a digital twin model that is a virtual copy of the physical ammonia cracking plant. This digital replica includes all components and their interactions, allowing comprehensive simulation of emergency scenarios without adding physical complexity to the actual plant. The digital twin enables reliable emergency management through virtual testing and analysis.
Solution Approach 2:
The system performs preliminary analysis by simulating multiple emergency scenarios in the digital twin model before they occur in the physical plant. Control scenarios are pre-generated and optimized through virtual testing, allowing the actual plant to respond more reliably to emergencies without complex real-time decision-making.
2Reliability
If digital twin model with real-time simulation is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The digital twin model serves multiple functions: it simulates normal operation, tests emergency scenarios, generates control strategies, and provides training capabilities. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, managing complexity while enhancing reliability.
3Reliability
If complex emergency scenario analysis is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
The system pre-generates control scenarios through simulation in the digital twin model before actual emergencies occur. By performing the complex analysis work in advance during normal operation, the system eliminates time delays when real emergencies need to be managed, improving both reliability and response time.
Data Source
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AI summary
The system according to the present disclosure includes a model generation module to generate a digital twin model including a replication model of a physical component of the ammonia cracking plant; an input module to input dynamic energy requirement and output information of an ammonia cracking apparatus included in the ammonia cracking plant into the digital twin model; an emergency condition imposition module configured to impose, for a preset emergency scenario, an emergency condition on the replication model of the physical component; a data collection and comparison module configured to collect data from the digital twin model under the imposed emergency condition, and compare first data when the emergency condition is imposed and second data when the emergency condition is not imposed; and a scenario generation module configured to generate a control scenario for the preset emergency scenario based on comparison results of the first data and the second data.