Dynamic Plant Model Generation from Static Models and P&ID Matching
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Solution Overview
Problem
Current software systems for plant design and simulation lack compatibility, requiring manual model conversion and significant labor and time to generate dynamic models from static simulators and piping and instrumentation diagrams, especially for large-scale plants.
Innovation Solution
An apparatus and method that automatically generates dynamic models by matching devices from static models and piping and instrumentation diagrams using a matching unit with model converting and integrating capabilities, reducing the need for manual intervention and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual model conversion is performed to generate dynamic models from static simulators and piping and instrumentation diagrams, then model compatibility between different software systems is achieved, but significant labor and time are required
Solution Approach 1:
The system performs preliminary actions by automatically converting static models and piping and instrumentation diagrams into dynamic models before the actual simulation process. The model generation unit pre-processes the data by matching device information, connection relationships, and parameters, eliminating the need for manual conversion during the simulation setup phase.
Solution Approach 2:
The invention introduces an intermediary model generation unit that acts as a mediator between static simulators and dynamic simulators. This unit automatically generates dynamic models by integrating information from static models and piping and instrumentation diagrams, serving as a bridge that eliminates the need for manual model conversion and ensures compatibility between different software systems.
2Reliability
If manual model conversion is performed to ensure software compatibility, then dynamic models can be generated for simulation, but the process requires high user proficiency and significant manual intervention
Solution Approach 1:
The system implements self-service by enabling automatic model generation without requiring user intervention. The model generation unit autonomously matches device information between static models and piping and instrumentation diagrams, extracts relevant parameters, and generates dynamic models. This eliminates the need for users to have specialized knowledge of dynamic simulators or to perform manual model conversion tasks.
Solution Approach 2:
The intermediary model generation unit handles the complexity of software compatibility automatically, shielding users from the need to understand complex model conversion processes. It manages the integration of static models and piping and instrumentation diagrams, ensuring reliable software compatibility while maintaining ease of operation.
3Productivity
If comprehensive device matching and model integration are performed automatically, then model generation efficiency is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the model generation process into distinct functional units: a model generation unit that performs device matching, a connection relationship extraction unit, and a parameter extraction unit. This modular segmentation improves productivity by enabling automated parallel processing while managing system complexity through clear separation of concerns and defined interfaces between units.
Data Source
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AI summary
To easily generate a dynamic model for performing a dynamic simulation of a plant, an apparatus, a method and a program are provided, the apparatus including a static model acquiring unit configured to acquire a static model indicating a steady state of the plant, a piping and instrumentation diagram data acquiring unit configured to acquire piping and instrumentation diagram data of the plant, and a matching unit configured to match a device included in the static model with a device included in the piping and instrumentation diagram data to identify a device-to-device correspondence relation.