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Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Calibration-free wavelength modulation spectroscopy approach for practical logging measurements of methane slip from natural gas engines.

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Related Experiment Video

Updated: Jul 2, 2026

Characterizing Far-infrared Laser Emissions and the Measurement of Their Frequencies
09:38

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An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

Mhanna Mhanna1, Jeremy Rochussen1, Patrick Kirchen1

  • 1Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, British Columbia V6T 1Z4, Canada.

ACS Sensors
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

A new laser sensor uses machine learning to accurately measure methane slip from natural gas engines. This innovation enables real-time monitoring for cleaner transport and environmental benefits.

Keywords:
emissions sensingmachine learningmarine vesselsmethane slipwavelength modulation spectroscopy

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Area of Science:

  • Environmental Science
  • Engineering
  • Sensor Technology

Background:

  • Natural gas (NG) is a sustainable transport fuel, but its greenhouse gas benefits depend on controlling methane slip.
  • Existing methane slip measurement methods are often costly, require frequent calibration, and struggle with dynamic engine conditions.
  • Accurate, real-time monitoring of methane (CH4) emissions is crucial for realizing the environmental advantages of NG vehicles.

Purpose of the Study:

  • To develop a novel, machine learning-enhanced laser-based sensor for rapid, accurate, and calibration-free methane slip measurement in engine exhaust.
  • To address the limitations of traditional methane slip monitoring techniques, including calibration needs, cost, and suitability for dynamic operations.
  • To provide a robust sensor system for real-world application in natural gas engines to support emission reduction strategies.

Main Methods:

  • Utilized wavelength modulation spectroscopy (WMS) with a distributed feedback (DFB) laser diode at 1.65 μm.
  • Employed a machine learning approach, specifically Gaussian process regression (GPR), to invert WMS signals, reducing computational cost and noise uncertainty.
  • Trained the GPR model on both simulated and measured WMS data for enhanced predictive accuracy.

Main Results:

  • The machine learning-enhanced sensor achieved a mean absolute percent error (MAPE) of 0.24% during model training.
  • Field testing on a natural gas marine vessel showed a mean absolute difference of 3.95% compared to reference Fourier transform infrared spectroscopy (FTIR) measurements.
  • The system demonstrated rapid, accurate, and calibration-free methane (CH4) measurement capabilities in dynamic exhaust conditions.

Conclusions:

  • The developed ML-enhanced WMS sensor represents a significant advancement for real-time methane slip monitoring in natural gas engines.
  • This technology offers reduced computational demands and improved accuracy, facilitating engine optimization and regulatory compliance.
  • Accurate methane slip data are essential for validating the environmental benefits of natural gas as a transport fuel and informing sustainable energy policies.