Plant Model Parameterization from Specified Output Values
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Solution Overview
Problem
Existing system models, particularly in industrial processes, require complex parameterization that often necessitates specialized knowledge, making them difficult to apply and simulate, especially for users outside the domain expertise.
Innovation Solution
A method and system for parameterizing system models that automatically determine input and parameter values based on specified output variable values, using physical model components and mathematical equations, allowing for accurate representation of system processes without manual expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physical models with detailed equations are used to represent plant components, then manufacturing precision and reliability are improved, but device complexity and difficulty of operation increase due to the large number of parameters that must be adjusted
Solution Approach 1:
The patent creates a simplified copy or representation of the complex physical model by using a reduced set of parameters that capture the essential behavior. Instead of working with the full complex model, a simplified version is constructed that reproduces key input-output relationships, making the model accessible to non-experts while maintaining sufficient accuracy for simulation purposes.
Solution Approach 2:
The patent extracts only the most critical parameters from the complex physical model that are necessary to represent the essential system behavior. By identifying and retaining only these key parameters while eliminating less important ones, the model becomes simpler to parameterize while still capturing the dominant physical phenomena.
2Manufacturing precision
If physical models with detailed equations are used, then manufacturing precision is improved, but ease of operation deteriorates as specialized knowledge is required for parameter adjustment
Solution Approach 1:
A simplified representation of the complex physical model is created that can be operated without specialized knowledge. This copied model structure maintains the essential input-output relationships while using parameters that can be determined from readily available data rather than requiring expert understanding of underlying physics.
Solution Approach 2:
The patent enables non-experts to parameterize the model themselves by providing automated methods for determining parameter values from operational data. Users can independently configure the model without requiring assistance from domain experts, as the system guides them through data collection and parameter calculation processes.
3Ease of operation
If black-box models are used to simplify parameterization, then ease of operation is improved, but manufacturing precision deteriorates as they cannot replicate behavior outside learned data ranges
Solution Approach 1:
The patent transforms the model representation by changing from a purely data-driven black-box approach to a hybrid approach that incorporates physical relationships. This is achieved by structuring the model to reflect known physical processes while using parameter estimation techniques to determine specific parameter values, thereby extending accuracy beyond the training data range.
Solution Approach 2:
The patent creates a composite modeling approach that combines elements of both physical models and black-box models. The model structure is based on physical principles and equations, while parameter values are determined through data-driven optimization, creating a hybrid model that leverages the strengths of both approaches.
4Ease of operation
If automated parameter determination methods are implemented, then ease of operation is improved, but device complexity increases due to the need for data processing systems
Solution Approach 1:
The patent introduces an intermediary data processing layer that automatically handles the complex tasks of parameter determination. This intermediary system processes operational data and calculates parameter values, acting as a mediator between raw data and the final model configuration, thereby shielding users from complexity while enabling automated parameterization.
Data Source
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AI summary
The present invention relates to a method (100) and a system (1) for parameterization of a plant model (10). A model (10) of a plant consisting of a plurality of model components (11), which represent physical sub-processes of the plant process and which correspond to physical models, is provided and at least one output value (A) is specified in an operating point of the plant. In addition, input values (E) and/or parameter values (P) and/or additional output values (A') of the sub-processes are determined on the basis of the model components (11) and the at least one specified output value (A).