Digital Twin Simulation Model Reuse Across Plant Lifecycle
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Solution Overview
Problem
Existing methods for generating simulation models for digital twins of process-based production plants are inefficient and do not facilitate consistent use across the plant's lifecycle, lacking integration of key components like actuators and control loops.
Innovation Solution
A computer-implemented method and system that generate simulation models by transforming a steady-state flow-driven model into a dynamic pressure-driven model, incorporating sensors, actuators, and control loops, enabling consistent use throughout the plant's lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple separate simulation models are developed for different lifecycle phases, then each phase can have specialized models, but the overall process requires repeated model development and is inefficient
Solution Approach 1:
The patent applies preliminary action by generating a comprehensive base simulation model during the planning phase that includes all necessary components (process components, sensors, actuators, control loops) before they are needed in subsequent lifecycle phases. This base model is then reused and extended in operational phases, eliminating the need to recreate models repeatedly and significantly reducing development time across the plant lifecycle.
Solution Approach 2:
The patent implements universality by creating a multi-functional simulation model that serves multiple purposes across different lifecycle phases. The same base model is used for process engineering design, virtual commissioning, operator training, and predictive control, with each phase utilizing the relevant components of the unified model rather than requiring separate specialized models.
2Stability of the object's composition
If a comprehensive simulation model including all components is created early, then consistency across lifecycle phases is improved, but the initial model development complexity and resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive simulation model into distinct functional components: process components, sensors, actuators, and control loops. Each component is developed and validated independently during the planning phase, then integrated into the unified model. This modular approach maintains model consistency across lifecycle phases while managing development complexity through systematic component-wise creation.
Solution Approach 2:
The patent uses preliminary action to perform the complex work of integrating all model components during the planning phase, when the plant design is still being developed and changes are easier to accommodate. By completing the comprehensive model integration early, the patent establishes a consistent foundation that can be reused throughout the lifecycle, avoiding the need to re-integrate components later when changes would be more costly and complex.
3Adaptability or versatility
If simulation models include detailed control loops and actuators, then the models can be used for virtual commissioning and predictive control, but the model generation process becomes more complex
Solution Approach 1:
The patent applies preliminary action by incorporating control loops and actuators into the simulation model during the planning phase, before the plant is physically built. This allows control strategies to be developed, tested, and optimized in advance using the same model that will later be used for virtual commissioning and predictive control. The control loop components are integrated early when their design is being finalized, ensuring consistency without requiring separate complexity management for different applications.
Solution Approach 2:
The patent implements universality by creating a unified simulation model that simultaneously supports multiple applications: process engineering design, virtual commissioning, operator training, and predictive control. The same model structure and components serve all these purposes, with each application utilizing the relevant aspects of the comprehensive model rather than requiring separate specialized models, thereby managing complexity through consolidation.
Data Source
AI summary
A method for generating, for a digital twin of a process of a production installation, a stationary flow-driven simulation model of a process and, based on this simulation model, a stationary pressure-driven simulation model of the process, wherein the model is used to generate a dynamic pressure-driven simulation model of the process, where each simulation model determines measurable state variables and characteristic values for product quality of the production installation based on material flows of feedstocks and operating media supplied to the production installation, where model data of each simulation model is generated and stored in memory, such that they readable by simulation software and used to execute simulation models, where the model is continually developed further and matched to respective applications without losing information from earlier phases or having to manually reenter information such that development of a digital process twin can be amortized over multiple incidents of use.