Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by a FPGA.

Area of Science:

• Analytical Chemistry
• Chemical Engineering
• Computational Science

Background:

• Measuring individual component concentrations in natural gas is challenging due to sensor cross-sensitivity.
• Existing methods struggle with accurate online detection of mixed gas components.

Purpose of the Study:

• To develop a mixed gas identification device for online natural gas detection.
• To utilize neural network technology to eliminate cross-sensitivity and accurately quantify gas components.
• To implement the neural network algorithm on a Field-Programmable Gate Array (FPGA) for enhanced performance.

Main Methods:

• Development of a mixed gas identification device employing a neural network algorithm.
• Implementation of the neural network algorithm on a Field-Programmable Gate Array (FPGA) for parallel computing and accelerated processing.
• Testing the device's accuracy and response speed for natural gas components.

Main Results:

• The neural network algorithm successfully eliminated cross-sensitivity issues among mixed gas components.
• Accurate recognition of methane, ethane, and propane concentrations was achieved.
• The device demonstrated a test error of less than 0.5% for methane and heavy alkanes within a 0-100% methane range.
• A rapid response speed of several seconds was recorded.

Conclusions:

• The developed neural network-based device offers a robust solution for online natural gas analysis.
• FPGA implementation provides a compact and fast platform for complex neural network computations.
• The system achieves high accuracy and speed, outperforming traditional methods in mixed gas analysis.