Method, device, and storage medium for measuring nitrogen content
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Solution Overview
Problem
Existing air separation units lack real-time detection of nitrogen content in the argon fraction, leading to nitrogen blockages and inefficient argon production due to the long detection periods of conventional analyzers like TCD or PDD, which hinder timely online control.
Innovation Solution
A soft-sensing method using a computer-based model that predicts nitrogen content in real-time by establishing an initial model with historical operation data, combining it with actual measurements for accurate and timely detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analyzers (TCD or PDD) are used for detecting nitrogen content, then detection accuracy is improved, but detection time becomes too long (10 to 30 minutes) to meet online control requirements
Solution Approach 1:
The patent creates a soft-sensing model that copies the detection function of conventional analyzers using readily available process data. Instead of using expensive specialized equipment (TCD/PDD), the system builds a mathematical model that replicates nitrogen content detection capabilities by processing existing operational parameters from the air separation unit, thereby achieving timely detection without specialized hardware.
Solution Approach 2:
The patent replaces the mechanical/physical analyzer system (TCD or PDD instruments requiring sampling, analysis, and reporting cycles) with a computational soft-sensing system. The soft-sensing model uses algorithmic processing of process data to substitute the physical measurement mechanism, enabling real-time nitrogen content estimation without the time delays inherent in conventional analyzer operation.
2Reliability
If no online detection is implemented, then device complexity is reduced, but nitrogen blockage cannot be prevented timely affecting production stability
Solution Approach 1:
The soft-sensing system utilizes existing process data and control systems already present in the air separation unit. Rather than adding independent detection equipment, the system leverages available operational parameters (temperatures, pressures, flow rates) that are already being measured and controlled, making the existing system serve the additional function of nitrogen content estimation without requiring separate dedicated measurement infrastructure.
Solution Approach 2:
The soft-sensing model serves multiple functions simultaneously: it estimates nitrogen content for blockage prevention, optimizes argon extraction rate, and provides process control guidance. By making the detection system multi-functional, the patent achieves comprehensive process optimization and reliability improvement without proportionally increasing system complexity, as the same model supports multiple control objectives.
3Productivity
If argon extraction rate is increased to improve production, then productivity is improved, but nitrogen content may become excessive causing blockage risk
Solution Approach 1:
The soft-sensing model provides continuous feedback on estimated nitrogen content levels, enabling real-time adjustment of extraction rates. The system monitors the relationship between extraction rate and nitrogen content, automatically guiding operational adjustments to maintain optimal extraction levels that maximize argon production while keeping nitrogen content below blockage thresholds, thus resolving the trade-off between productivity and blockage risk.
Data Source
AI summary
Disclosed are a method, a device, and a storage medium for measuring the nitrogen content. The method includes determining related variables associated with the nitrogen content; establishing an initial model, wherein the initial model takes the related variables as independent variables and the nitrogen content as dependent variables according to a historical operation data, and calculating parameters in the initial model; according to parameters in the initial model, obtaining parameters in a soft-sensing model by fitting; according to detected values of the related variables and the soft-sensing model, predicting the nitrogen content in real-time; and according to the predicted nitrogen content and an actual nitrogen content obtained by periodic sampling, obtaining a fusion detection result.

