Ammonia Cracking Plant Digital Twin for Emergency Control
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Solution Overview
Problem
Existing systems for managing ammonia cracking plants face challenges in ensuring stable operation during emergency situations, primarily due to complex interactions between multiple components which are difficult to describe using conventional methods.
Innovation Solution
A system and method utilizing a digital twin model to simulate and analyze the ammonia cracking plant's operations, including modules for generating and updating the digital twin model, inputting dynamic energy requirements, imposing emergency conditions, collecting and comparing data, and generating control scenarios to ensure stability and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods are used to manage ammonia cracking plant operations, then the system structure is simple and easy to implement, but the ability to describe and manage complex interactions between multiple components is insufficient
Solution Approach 1:
The patent creates a digital twin model that is a virtual copy of the physical ammonia cracking plant, including replicas of all components and their interactions. This digital copy enables complex simulations and analysis without modifying the actual plant structure, thus maintaining simplicity while gaining analytical capability.
Solution Approach 2:
The system performs preliminary simulations of emergency scenarios and operational variations on the digital twin model before implementing changes in the physical plant. This allows prediction of system behavior and optimization of control strategies in advance, improving adaptability without increasing physical complexity.
2Reliability
If real-time monitoring and simulation of all plant components is implemented, then operational stability and safety are improved, but computational requirements and system complexity increase
Solution Approach 1:
The digital twin model segments the ammonia cracking plant into discrete component models (reactors, separators, heat exchangers, etc.), each with its own mathematical representation. This segmentation allows selective simulation of specific components or subsystems, reducing computational burden while maintaining overall system reliability.
Solution Approach 2:
The system applies partial simulation action by focusing computational resources on critical components and emergency scenarios rather than continuously simulating all plant operations at full detail. Normal operations use simplified models, while emergency conditions trigger more detailed simulations only where needed.
3Reliability
If digital twin modeling and comprehensive simulation are used to predict emergency scenarios, then safety and response accuracy are improved, but implementation cost and initial complexity increase
Solution Approach 1:
The digital twin model serves multiple functions: it monitors normal operations, simulates emergency scenarios, optimizes control strategies, and trains operators. This multi-functionality justifies the initial implementation investment by providing comprehensive safety and operational benefits from a single system.
Solution Approach 2:
The system continuously compares digital twin predictions with actual plant measurements, using the discrepancies to refine and update the digital model. This feedback mechanism improves accuracy over time and validates the safety predictions, making the system more convincing and easier to justify as implementation progresses.
Data Source
AI summary
The system according to the present invention 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 to impose, for a preset emergency scenario, an emergency condition on the replication model of the physical component; a data collection and comparison module 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 to generate a control scenario for the preset emergency scenario based on comparison results of the first data and the second data.