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Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric

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This study uses machine learning to infer pressure from HCl infrared spectra. The ExtraTrees algorithm achieved high accuracy, offering a noninvasive method for pressure retrieval in harsh environments.

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Chemical Sensing

Background:

  • Accurate pressure measurement is crucial in many scientific and industrial applications.
  • Traditional pressure sensors can be invasive or unsuitable for corrosive environments.

Purpose of the Study:

  • To develop a noninvasive machine learning (ML) approach for inferring pressure.
  • To analyze infrared spectral lines of hydrogen chloride (HCl) for pressure retrieval.

Main Methods:

  • Simulated high-resolution HCl spectra using the HITRAN database across varied pressures, temperatures, and optical paths.
  • Extracted Voigt profile parameters to train six ML models, including ExtraTrees.
  • Validated the best-performing model with experimental spectra.

Main Results:

  • The ExtraTrees algorithm achieved a Root Mean Square Error (RMSE) of 23.95 mbar on synthetic data.
  • Experimental validation showed strong agreement at lower pressures, with errors below 5% (e.g., 2.62% at 78 mbar).

Conclusions:

  • The hybrid methodology combining simulated training and experimental validation provides a reliable, noninvasive pressure retrieval method.
  • This approach avoids direct sensor exposure to corrosive environments.