Reconfigurable Digital Twin Platform for Multi-Stage Process Simulation
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Solution Overview
Problem
Current analytical techniques for simulating and predicting multi-stage processing facilities oversimplify component logic and lack a holistic, reconfigurable digital platform that can be easily adapted and controlled without requiring advanced data analytic and computer skills.
Innovation Solution
A reconfigurable digital platform that uses reusable models to build digital representations of processing components, allowing for flexible configuration of interconnections and signaling between them, enabling real-time simulation and prediction of key performance indicators through a graphical user interface and predictive models trained with historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulations are specifically developed for individual processing components, then the accuracy of component-level analysis is improved, but the complexity of creating a holistic system-level digital platform increases
Solution Approach 1:
The system divides the complex industrial operation into discrete processing components, each represented by an executable digital agent. These agents can be individually configured and simulated, then assembled into system-level models. This segmentation allows accurate component-level analysis while managing system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal digital agent framework that can represent multiple types of processing components through a common interface and execution model. The same digital agent infrastructure handles diverse components (reactors, separators, heat exchangers, etc.), providing system-level functionality without requiring separate specialized systems for each component type.
2Measurement precision
If detailed analytical components are used to accurately represent processing components, then the simulation accuracy is improved, but the ease of adapting and reconfiguring the digital platform deteriorates
Solution Approach 1:
The digital agents are designed with dynamic configuration capabilities, allowing their parameters, logic, and interconnections to be modified at runtime without requiring complete system re-development. This dynamic nature enables both high simulation accuracy through detailed modeling and easy adaptability through reconfigurable digital twins that can be updated to reflect changing plant conditions.
3Adaptability or versatility
If reusable models are used to build digital representations, then the ease of adapting to various industrial contexts is improved, but the complexity of managing model interconnections and signaling increases
Solution Approach 1:
The patent introduces a runtime environment and standardized communication protocols as intermediaries between digital agents. These intermediaries manage the complexity of interconnections and signaling by providing uniform interfaces, event handling mechanisms, and data exchange standards, allowing reusable models to be composed without directly managing the complexity of their interactions.
4Loss of information
If a holistic system-level digital platform is created, then the operational insights and control capabilities are improved, but the requirement for advanced data analytic and computer skills increases
Solution Approach 1:
The system creates digital copies (digital twins) of physical processing components and systems that can be manipulated, analyzed, and controlled without requiring direct expertise in the underlying complex analytical models. Users interact with simplified digital representations that preserve the essential behavior and insights of the physical systems while being more accessible to operators without advanced analytical skills.
Data Source
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AI summary
This disclosure relates to a reconfigurable simulative and predictive digital assistant/platform for simulation of a multi-stage processing facility. The digital assistant generates and assembles digital representations of the individual physical processing stages and components of the multi-stage processing facility in a reconfigurable manner according to a set of configuration commands generated using user inputs in a graphical user interface. At least one of the digital representations include a reusable predictive model that is trained when the digital representation is generated by the digital assistant. The digital assistant further performs simulation of the multi-state processing facility "as is" or in alternative "what-if" scenarios by simulating the digital representations according to a set of timing signals in the set of configuration commands.