Layered Digital Twin Modeling for Plant Asset Optimization
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Solution Overview
Problem
Current digital twin modeling solutions are either inaccurate and inexpensive or accurate but costly and difficult to implement, especially in complex industrial settings with varying asset performance over time, making it challenging to model and optimize industrial processes effectively.
Innovation Solution
A digital twin modeling system that generates a customizable, scalable, and affordable model of an industrial plant using a multi-layer structure incorporating physical and virtual data, allowing for accurate monitoring and optimization of asset performance by simulating changes before implementation, with a cloud-based tool that requires minimal technical expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accurate digital twin modeling is implemented in complex industrial settings, then modeling accuracy improves, but implementation cost and difficulty increase
Solution Approach 1:
The patent segments the digital twin model into multiple hierarchical layers (process layer, asset layer, component layer) that can be independently developed and configured. Each layer focuses on specific aspects of the industrial process, allowing teams to build accurate models incrementally without overwhelming complexity. This layered approach enables accurate modeling by breaking down the complex system into manageable segments that can be validated and refined separately.
Solution Approach 2:
The patent introduces a temporal dimension to the digital twin model by incorporating historical data, real-time data, and predictive simulations across different time periods. This multi-temporal approach allows the model to capture asset performance degradation over time, maintenance history, and future predictions, significantly improving modeling accuracy without proportionally increasing implementation complexity through standardized temporal frameworks.
2Productivity
If comprehensive asset monitoring is implemented, then process optimization improves, but system cost increases
Solution Approach 1:
The patent implements partial monitoring by focusing computational resources on critical assets and parameters that have the greatest impact on process efficiency. Rather than monitoring every asset at maximum detail, the system dynamically adjusts monitoring intensity based on asset criticality, operational conditions, and predicted failure risks. This approach achieves significant process optimization while controlling system costs by applying comprehensive monitoring only where necessary.
Solution Approach 2:
The digital twin model incorporates predictive maintenance capabilities that automatically identify when assets require servicing based on simulated performance degradation. The system self-adjusts monitoring intensity and triggers maintenance alerts without constant human intervention, optimizing process efficiency while reducing the operational costs of comprehensive monitoring through automated, condition-based resource allocation.
3Loss of information
If virtual instruments are added to locations without physical instruments, then measurement coverage improves, but model complexity increases
Solution Approach 1:
The patent uses virtual instruments as intermediary elements that bridge gaps where physical instruments are absent. These virtual instruments don't directly measure physical quantities but instead infer measurements through mathematical models that combine data from nearby physical sensors and process knowledge. This intermediary approach expands measurement coverage to previously unmonitored locations while managing model complexity through standardized virtual instrument templates and physics-based inference models.
Data Source
AI summary
A digital twin model based plant operation and optimization is provided. Systems and methods of the solution can receive data on assets of a plant, the assets' topology, connectivity and flow and deployed physical instruments along with the measurements of the physical instruments. The solution generate a model having a plurality of layers based on the received data and provide one or more virtual instruments in the model. The solution can determine, based on a set of relationships for interactions between assets and the received measurements input into the model, one or more virtual measurement for the one or more virtual instruments and generate, responsive to a comparison with a threshold, a notification to service at least one of the assets at the plant.