Hybrid Module Simulation for Accurate Modular Plant Operation
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Solution Overview
Problem
Current simulations for modular industrial plants lack accuracy and require high manual effort, failing to adequately represent real behavior and properly calibrate control parameters, leading to inefficiencies and increased costs.
Innovation Solution
A combination of knowledge-based and data-driven enhancement methods is applied to initial simulations, incorporating domain knowledge and real-world data to refine models, ensuring accurate and efficient simulation of module operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional simulation methods are used for modular industrial plants, then manual effort and time are reduced, but simulation accuracy and reliability deteriorate
Solution Approach 1:
The patent combines white-box simulation (physics-based models) and black-box simulation (data-driven models) into a unified hybrid simulation framework. This merging allows the system to leverage both the interpretability of physics-based models and the accuracy of data-driven models, thereby improving simulation reliability without increasing manual effort.
Solution Approach 2:
The patent introduces a digital twin as an intermediary between physical plant data and simulation models. The digital twin serves as a mediator that automatically integrates real-time operational data with simulation parameters, eliminating the need for manual model calibration while maintaining high simulation accuracy.
2Ease of operation
If simplified simulation models are used, then computational speed and ease of operation improve, but measurement precision and manufacturing precision deteriorate
Solution Approach 1:
The patent implements dynamic model complexity adjustment where the simulation model automatically adapts its detail level based on the operational context. For routine operations, simplified models are used for speed; for critical operations requiring high precision, the system automatically switches to more detailed models, maintaining both ease of operation and measurement precision.
Solution Approach 2:
The patent applies different levels of model fidelity to different parts of the simulation based on their importance. Critical subsystems that require high precision are modeled with greater detail, while less critical subsystems use simplified models, optimizing the balance between computational efficiency and simulation precision.
3Reliability
If comprehensive simulation models are used, then simulation accuracy improves, but device complexity and manual effort increase
Solution Approach 1:
The patent implements automated model generation and calibration systems that self-adjust simulation parameters based on incoming operational data. The system automatically performs model validation, parameter optimization, and updates without requiring manual intervention, thereby achieving high simulation accuracy while keeping the operational complexity low.
Solution Approach 2:
The patent performs preliminary model preparation and validation during the design phase, creating pre-configured simulation models that are ready for deployment. This preliminary action reduces the complexity of model management during operation, as the models are already optimized and validated before actual use.
4Productivity
If traditional simulation approaches are used, then implementation speed improves, but adaptability and versatility deteriorate
Solution Approach 1:
The patent develops a universal simulation framework that can handle multiple types of industrial plant modules and operations through a common interface. The hybrid simulation approach and digital twin architecture enable the same system to adapt to different plant configurations, processes, and operational scenarios without requiring separate specialized models, thereby achieving both fast deployment and high adaptability.
Data Source
AI summary
Computer-implemented method for simulating module operation of a module of a modular industrial plant include 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.