Factory Gas Prediction Using Learning Models Without Steam Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting steam usage in factories are limited by the need to measure steam pressure and flow velocity, making them impractical in certain situations.
Innovation Solution
A gas amount prediction method using a learning model to calculate the usable gas generation amount based on past operation data, including a use rate calculation and consideration of steam loss due to heat dissipation, without requiring direct measurement of steam pressure or flow velocity, and incorporating features like sea-level pressure for variation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correction coefficient methods using steam pressure or flow velocity measurements are employed, then prediction accuracy of usable gas amount is improved, but measurement complexity and device requirements increase
Solution Approach 1:
The patent replaces physical measurement systems (pressure sensors, flow velocity meters) with a data-driven learning model that processes operational data to predict usable gas amounts. This substitution eliminates the need for complex physical measurements while maintaining prediction accuracy through pattern recognition in historical operational data.
Solution Approach 2:
The patent creates a virtual model (learning model) that replicates the relationship between gas generation and actual usability based on historical operational data. This virtual copy allows prediction without requiring physical measurement devices, effectively copying the behavior patterns from past operations to forecast future usability.
2Measurement precision
If learning models using comprehensive operational data are used, then prediction accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary learning and model training using historical operational data before actual prediction is needed. This advance preparation creates a ready-to-use prediction model that can quickly generate results without requiring complex real-time computations, separating the heavy computational work from the prediction execution.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
A gas amount prediction method is a method for predicting an amount of gas generated in a factory, and includes a generation amount calculation step of calculating a generation amount of actually usable gas by using a learning model that has learned a relationship between the generation amount of the gas and a use amount of the gas in past operation data.