Plant Scale-Up Predictive Modeling from Reduced-System Data
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Solution Overview
Problem
The existing method of constructing large-scale plants by scaling up from small to medium and finally large scales requires creating predictive models at each stage, which is time-consuming and costly, slowing down the development process.
Innovation Solution
An assistance method and device that acquire information from a reduced system and generate predictive models for intermediate and large-scale systems based on relationships between systems of different scales, allowing for automatic model generation and reduced design time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are created and tuned at each scale (small, medium, large) to ensure accurate process prediction, then model accuracy and reliability are improved, but development time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by establishing scaling relationships and mathematical models in advance that can predict large-scale plant behavior based on small-scale data. The scaling laws and dimensionless numbers are developed beforehand, allowing direct extrapolation from small-scale experimental data to large-scale plant predictions without requiring intermediate model tuning at each scale.
Solution Approach 2:
The patent uses copying by creating virtual models through mathematical scaling relationships that replicate the behavior of large-scale systems based on small-scale physical systems. The scaling models serve as copies or representations that allow predictions to be made without physically constructing and testing intermediate-scale systems.
2Reliability
If predictive models are created and tuned at each scale to ensure accurate process prediction, then model reliability is improved, but development cost increases significantly
Solution Approach 1:
The patent applies preliminary action by establishing scaling relationships and mathematical models in advance that can predict large-scale plant behavior based on small-scale data. The scaling laws and dimensionless numbers are developed beforehand, allowing direct extrapolation from small-scale experimental data to large-scale plant predictions without requiring intermediate model tuning at each scale.
Solution Approach 2:
The patent uses copying by creating virtual models through mathematical scaling relationships that replicate the behavior of large-scale systems based on small-scale physical systems. The scaling models serve as copies or representations that allow predictions to be made without physically constructing and testing intermediate-scale systems.
3Measurement precision
If detailed model tuning is performed at each scale to achieve accurate predictions, then prediction accuracy is improved, but the complexity of the design process increases
Solution Approach 1:
The patent applies parameter changes by using dimensionless numbers and scaling parameters that transform the complexity of full-scale system modeling into simplified relationships. By changing the parameters from absolute values to dimensionless ratios, the patent reduces the number of variables that need to be tuned while maintaining prediction accuracy across different scales.
Solution Approach 2:
The patent applies universality by creating scaling relationships that are applicable across all scales from small to large systems. The dimensionless numbers and scaling laws developed can be universally applied to different plant sizes and configurations, reducing the need for scale-specific model tuning and simplifying the overall design process.
Data Source
AI summary
An assistance method comprising steps of: acquiring information related to a process or a reduced system when the process to be performed in a plant is performed in the reduced system with a smaller scale than the plant; and generating at least a part of a model for predicting a process to be performed in the plant, or an intermediate system with a scale between the plant and the reduced system, based on a relationship between systems when the process is performed in a plurality of systems of different scales, from the information.