Deep learning-based multi-parameter coupling compensation algorithm for clamp-on gas metering systems.

Area of Science:

• Engineering
• Computer Science
• Physics

Background:

• Clamp-on gas metering systems face significant accuracy challenges due to complex interdependencies between temperature, pressure, and density.
• Traditional compensation methods struggle with nonlinear coupling effects, leading to errors in dynamic industrial environments.

Purpose of the Study:

• To develop a novel deep learning algorithm for multi-parameter coupling compensation in clamp-on gas metering.
• To enhance measurement accuracy by effectively modeling nonlinear parameter interdependencies.

Main Methods:

• A hybrid Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architecture was employed.
• Real-time adaptive correction mechanisms, including sliding window processing and dynamic weight adjustment, were incorporated.
• The algorithm simultaneously models temporal dependencies and spatial relationships within multi-parameter data.

Main Results:

• Achieved an average measurement error of 0.52%, a 78% improvement over conventional linear compensation (2.45% error).
• Demonstrated long-term stability over 720 hours of continuous operation.
• Real-time processing capability of 5.34 milliseconds, suitable for industrial applications.

Conclusions:

• The proposed deep learning algorithm significantly enhances the accuracy and reliability of clamp-on gas metering.
• This method offers a practical solution for high-precision gas flow measurement in complex industrial conditions.
• The research advances precision gas flow measurement technology through advanced AI-driven compensation.