Grey-Box Module Simulation for Modular Plant Commissioning
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Solution Overview
Problem
Current simulations of modular industrial plant modules lack accuracy and efficiency, leading to suboptimal operation and increased costs due to incomplete representation of module behavior and control parameters.
Innovation Solution
A computer-implemented method combining white-box and black-box simulations with knowledge-based and data-driven enhancements, including domain knowledge enforcement and data assimilation, to create a grey-box simulation model that accurately represents module operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If white-box or black-box simulation is used for module simulation, then simulation can be performed, but simulation accuracy is insufficient
Solution Approach 1:
The patent combines white-box simulation (based on physical principles and domain knowledge) with black-box simulation (based on data-driven approaches) to create a hybrid grey-box simulation model. This merging allows the system to leverage both the interpretability and physical consistency of white-box models and the adaptability and accuracy of black-box models, thereby improving overall simulation accuracy while managing complexity through integrated model management.
Solution Approach 2:
The simulation model uses a composite structure combining different modeling approaches (white-box components based on domain knowledge and black-box components based on measurement data) similar to how composite materials combine different substances to achieve superior properties. This composite modeling strategy enables the system to capture both physical principles and empirical behaviors, improving simulation fidelity without requiring a single overly complex model.
2Loss of information
If domain knowledge is enforced through knowledge-based enhancement, then simulation completeness improves, but model complexity increases
Solution Approach 1:
The system performs knowledge-based enhancement by pre-integrating domain knowledge (physical principles, operational constraints, safety rules) into the simulation model before actual simulation execution. This preliminary action ensures that the model structure already incorporates essential domain information, reducing the need for complex real-time computations and enabling more complete simulations without proportionally increasing operational complexity.
Solution Approach 2:
The patent implements feedback mechanisms where simulation results are continuously compared against domain knowledge and measurement data, allowing the model to self-correct and refine its parameters. This feedback loop ensures simulation completeness by validating results against known physical principles and operational constraints, while the iterative nature of feedback allows complexity to be managed through gradual refinement rather than monolithic model structures.
3Measurement precision
If data-driven enhancement is performed using measurement data, then simulation accuracy improves, but data processing requirements increase
Solution Approach 1:
The system applies data-driven enhancement selectively to specific parts of the simulation model where measurement data is most valuable, rather than uniformly across the entire model. This local quality approach allows the system to focus computational resources on enhancing accuracy in critical areas (such as regions with high uncertainty or important operational parameters) while using simpler modeling approaches in less critical areas, thereby improving overall accuracy without proportionally increasing data processing requirements.
4Loss of time
If module simulation is performed before actual installation, then time and cost efficiency improves, but simulation reliability must be ensured
Solution Approach 1:
The system performs virtual commissioning and verification simulations before actual module installation and deployment, allowing operational procedures, control logic, and system interactions to be tested and optimized in advance. This preliminary action identifies and resolves potential issues virtually, reducing commissioning time and avoiding costly rework during actual installation, while simulation reliability is maintained through the hybrid modeling approach that incorporates both physical principles and empirical validation.
Data Source
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AI summary
The invention provides computer-implemented methods for simulating module operation of a module of a modular industrial plant, the method comprising: providing an initial model for the module based on a simulation, e.g., a white-box simulation or a black-box simulation or a grey-box simulation; performing a knowledge-based enhancement step or a data-driven enhancement step comprising obtaining an enhanced model; and simulating module operation of the module by means of the enhanced model.