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

## TL;DR

This paper introduces a machine learning method to estimate pressure by analyzing HCl spectral lines, avoiding direct exposure to corrosive environments.

## Contribution

A novel noninvasive ML approach for pressure retrieval using HCl spectral line parameters and simulated training data.

## Key findings

- The ExtraTrees model achieved an RMSE of 23.95 mbar on synthetic data.
- Experimental validation showed less than 5% error at lower pressures (e.g., 2.62% at 78 mbar).
- The hybrid method avoids sensor exposure to corrosive environments.

## Abstract

This study proposes
a noninvasive machine learning approach
to
infer pressure by analyzing the infrared spectral lines of the HCl
molecule. High-resolution spectra were simulated using the HITRAN
database across various pressures (15–900 mbar), temperatures
(273–373 K), and optical paths (1–10.5 cm). Voigt profile
parameters (amplitude, center, height, and Gaussian/Lorentzian widths)
were extracted from these spectral lines and used to train six ML
models. The ExtraTrees algorithm demonstrated superior performance,
achieving an RMSE of 23.95 mbar on synthetic data. Validation with
experimental spectra (78–790 mbar, 293 K) revealed strong agreement
at lower pressures, with errors below 5% (e.g., 2.62% at 78 mbar).
The hybrid methodology, which combines simulated training with experimental
validation, circumvents the need for direct sensor exposure to corrosive
environments and offers a reliable alternative for pressure retrieval.