Reconfigurable Digital Twins for Multi-Stage Facility What-If Simulation
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Solution Overview
Problem
Current analytical techniques for simulating and predicting multi-stage processing facilities oversimplify component logic and are not easily adaptable or reconfigurable, lacking a holistic digital platform that can simulate, predict, and control these facilities without requiring advanced data analytic and computer skills.
Innovation Solution
A reconfigurable digital platform that uses discrete event simulation and predictive models, allowing for the creation of digital replicas of multi-stage processing facilities, enabling flexible simulation and prediction of key performance indicators through reusable models and graphical user interfaces for configuring digital representations and interconnections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current analytical techniques are used to simulate and predict multi-stage processing facilities, then simulations can be developed for individual processing components, but the system lacks holistic system-level simulation capability and is not easily adaptable or reconfigurable
Solution Approach 1:
The system segments the multi-stage processing facility into discrete process entities, each represented by executable digital representations (digital twins). Each digital representation encapsulates specific process logic, parameters, and behavior for individual components or stages, allowing modular simulation and easy reconfiguration by adding, removing, or modifying individual digital representations without affecting the entire system.
Solution Approach 2:
The platform implements a universal executable instruction set and configuration file format that can represent diverse processing components through a common framework. The system uses standardized data structures and interfaces that allow different types of process entities to be simulated using the same underlying architecture, enabling holistic system-level simulation while maintaining adaptability across various industrial applications.
2Ease of manufacture
If over-simplified logic is used for individual components, then simulations can be developed more easily, but the accuracy and realism of component representation deteriorates
Solution Approach 1:
The system employs dynamic configuration files that can be modified at runtime to adjust the complexity and detail of process logic for each digital representation. Users can enhance component accuracy by adding detailed process equations, constraints, and conditional logic to specific digital representations without redesigning the entire simulation framework. The executable instruction set allows flexible adjustment of logic depth based on simulation needs.
Solution Approach 2:
The platform uses configurable parameters within each digital representation to control the level of logical detail and computational complexity. Configuration files allow users to specify process parameters, logic depth, and simulation fidelity for individual components, enabling the system to adapt between simplified and detailed representations as needed for different simulation scenarios and accuracy requirements.
3Reliability
If specialized data analytic and computer skills are required to use the simulation system, then advanced analytical capabilities are available, but the system becomes difficult to operate for general users
Solution Approach 1:
The system creates digital replicas (digital twins) of physical processing components that mirror their behavior and characteristics. These digital representations encapsulate complex analytical models and data processing capabilities while presenting a simplified interface through configuration files and standardized instruction sets. Users can configure and operate simulations without needing to understand the underlying complex data analytics, as the digital representations handle the analytical processing automatically.
Solution Approach 2:
The platform introduces an intermediary layer consisting of configuration files and standardized executable instructions that mediate between user operations and complex analytical processing. This intermediary framework translates high-level user configurations into detailed simulation commands, shielding users from complex data analytic requirements while maintaining access to advanced analytical capabilities through the standardized interface.
Data Source
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.