Knowledge-Based Digital Twin Workflows for Plant Optimization
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Solution Overview
Problem
Existing approaches for building digital twins for industrial plants require extensive collaboration among industry domain experts, process modeling engineers, and data scientists, leading to a time-consuming and non-scalable process, as each type of industry and specific plant necessitates starting from scratch, making it difficult to reproduce and optimize effectively.
Innovation Solution
A processor-implemented method and system that uses knowledge-based engineering to identify high-level problem statements, derive detailed technical problem definitions, and build digital twins by executing knowledge-guided workflows, combining physics-based and data-based models to create an integrated digital twin for solving plant monitoring and optimization challenges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing approaches for building digital twins are used, then digital twin solutions can be created with domain expert knowledge, but the process is time-consuming and requires extensive collaboration among multiple experts
Solution Approach 1:
The system performs preliminary actions by pre-defining problem type specific workflows and storing them in a workflow repository before actual digital twin development is needed. These workflows include problem definition templates, data processing procedures, and model generation steps that have been prepared in advance based on common digital twin development patterns across different industries
Solution Approach 2:
The system creates reusable digital twin templates and workflow patterns that can be copied and adapted across different industries and plant types. Instead of building digital twins from scratch for each case, the system stores successful digital twin configurations and workflows in a repository, allowing teams to replicate proven solutions while adapting to specific requirements
2Adaptability or versatility
If custom digital twin solutions are built for each specific plant, then the solution can be tailored to specific requirements, but the process is not easily reproducible and requires reinvestment of effort
Solution Approach 1:
The system creates universal digital twin templates and workflows that can serve multiple industries and plant types simultaneously. The problem type specific workflows are designed to be industry-agnostic yet adaptable to specific requirements, allowing the same framework to be applied across power generation, manufacturing, and other sectors with minimal customization
Solution Approach 2:
The system segments the digital twin development process into modular problem type specific workflows that can be independently selected, configured, and reused. Each workflow represents a discrete functional unit (e.g., predictive maintenance, process optimization, quality control) that can be assembled like building blocks to create customized solutions without rebuilding the entire system
3Adaptability or versatility
If comprehensive digital twin models are built with all possible features, then the model can handle diverse problems, but the complexity of building and maintaining the model increases
Solution Approach 1:
The system implements dynamic workflow selection where the complexity of the digital twin model adapts to the specific problem being solved. Instead of maintaining a single comprehensive model with all possible features, the system dynamically assembles appropriate workflows from the repository based on the problem type, data availability, and required outcomes, keeping the active model complexity manageable while maintaining access to comprehensive capabilities when needed
Data Source
AI summary
Existing approaches for building digital twins specific to industrial plants require industry domain experts, process modeling engineers, data scientists, and solution developers to spend considerable time and effort to build the right solution. This is not an easily reproducible process. For each type of industry and for each specific plant, the design, and development process must start all over, more or less from scratch and the effort needs to be reinvested. Hence this is not a scalable proposition. Method and system disclosed herein provide a knowledge-based plant monitoring and optimization approach. In this approach, for a given high-level problem statement, a detailed problem definition is derived, a plant view of interest is identified using the knowledge based approach, and in turn plant data of interest is identified. Further, a digital twin is generated using the plant data of interest, which is then used for the plant monitoring and optimization.