Physics-Informed Digital Twin Modeling for Dynamic Plant Adaptation
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Solution Overview
Problem
Building and maintaining predictive models for industrial digital twins is challenging due to inconsistent and limited industrial data, which hinders learning from multiple sources and adapting to dynamic conditions, requiring time-consuming and expertise-intensive processes.
Innovation Solution
A processor-implemented method and system that normalizes and parameterizes data using critical influential variables to create Physics Informed Digital Twin (PIDT) models, homogenizes child models, and constructs a master PIDT model, enabling adaptation to dynamic conditions and learning from diverse sources with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physics-guided data-driven modeling is used to build digital twin models, then model accuracy and consistency with physical principles is improved, but time consumption and expertise requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing data and governing equations, normalizing them, and parameterizing with critical influential variables before model construction. This preparation work is done in advance to reduce the time required during actual model building and adaptation phases.
Solution Approach 2:
The model construction is segmented into multiple independent components: child PIDT models are created for different governing equations, then homogenized, and finally assembled into a master PIDT model. This segmentation allows parallel processing and reduces overall construction time while maintaining model accuracy.
2Adaptability or versatility
If traditional digital twin models are built without adaptation mechanisms, then initial model development is simpler, but the models cannot adapt to dynamic conditions and require complete re-training when conditions change
Solution Approach 1:
The master PIDT model dynamically adapts to changing conditions by receiving real-time inputs and adjusting its predictions accordingly. The model structure allows for continuous adaptation without requiring complete re-training, as it can incorporate new data and adjust its parameters while maintaining its learned relationships from the child models.
Solution Approach 2:
The master PIDT model serves multiple functions: it can handle different governing equations through its child models, adapt to various dynamic conditions, and provide predictions across different operating scenarios. This multi-functionality is achieved through a unified architecture that integrates multiple specialized child models.
3Reliability
If multiple data sources are used to improve model learning, then data quality and model robustness improve, but data inconsistency from different sources creates difficulties in learning
Solution Approach 1:
The system achieves homogeneity by normalizing data from multiple sources to a common format and scale. The pre-processing step ensures that data from different equipment, time scales, and sources are transformed into a consistent representation, eliminating inconsistencies while preserving the unique information from each source.
Solution Approach 2:
The system introduces an intermediary layer of critical influential variables (CIVs) that mediate between raw data from multiple sources and the model learning process. These CIVs serve as a standardized interface that translates diverse data formats into a common language that the model can learn from effectively.
Data Source
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AI summary
A method and system that can enable quick development of Physics Informed Digital Twin, PIDT, models that are generalized, do not need re-training for dynamic conditions in the industrial plants, can learn from multiple data sources, equipment, and materials by receiving data (202) related to a design and one or more materials of one or more industrial equipment, one or more operating conditions, and one or more governing equations related to the one or more industrial equipment or process wherein the plurality of data related to design comprise make, type, dimensions of the equipment and the specific design aspects of the equipment. Segregating the data based on critical influencing variables, CIV, (204). Generating design of experiments using the CIV (206). Building PIDT models for each design of CIVs (208). Building homogenised child PIDT models (214). Extracting learning parameters (216) and building master IPDT models with usign the learning parameters (218).