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Updated: Jun 30, 2026

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
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Machine Learning-Enhanced NDIR Methane Sensing Solution for Robust Outdoor Continuous Monitoring Applications.

Yang Yan1, Lkhanaajav Mijiddorj1, Tyler Beringer1

  • 1School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

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A new low-cost AIMNet Sensor uses machine learning to accurately detect methane (CH4) gas, even outdoors. This multi-sensory instrument provides reliable CH4 leak detection for environmental monitoring networks.

Area of Science:

  • Environmental Science
  • Sensor Technology
  • Machine Learning

Background:

  • Accurate methane (CH4) monitoring is crucial for environmental protection and industrial safety.
  • Existing gas detection instruments often face challenges with outdoor operation due to environmental fluctuations.
  • Developing low-cost, high-performance, and field-deployable sensors is essential for distributed monitoring networks.

Purpose of the Study:

  • To develop a low-cost, compact, and high-performance multi-sensory gas detection instrument (AIMNet Sensor).
  • To integrate machine learning algorithms for accurate data processing and environmental compensation.
  • To validate the instrument's performance in real-world field conditions for methane leak detection.

Main Methods:

  • The AIMNet Sensor integrates a non-dispersive infrared (NDIR) gas sensing unit and a BME280 environmental sensor.
Keywords:
CH4 emissionsNDIR gas sensorartificial neural networkfield CH4 monitoringmachine learning

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  • Machine learning regression models, including Multilayer Perceptron (MLP) and Elastic Net, were trained on 13,125 calibration data points.
  • Field mobile validation was conducted near a wastewater management facility, comparing results with LI-COR reference measurements.

    • Main Results:

    • MLP and Elastic Net models achieved high accuracy (R2>0.8) in both indoor and outdoor scenarios.
    • Inter-sensor root mean square error (RMSE) was within 1.5 ppm across identical instruments.
    • The AIMNet Sensor demonstrated reliable detection of CH4 leaks up to 18 ppm, showing strong correlation with reference measurements.

    Conclusions:

    • The machine learning-integrated NDIR sensing solution (AIMNet) offers a practical and scalable approach for methane monitoring.
    • The developed instrument addresses outdoor operation challenges caused by environmental fluctuations.
    • AIMNet provides a robust solution for distributed CH4 monitoring networks in real-world field applications.