Artificial Intelligence Application in Photoacoustic of Gases

Abstract

Photoacoustic spectroscopy is a powerful, non-destructive, ultrasensitive technique that covers a wide range of applications including atmospheric monitoring, industrial, environmental, and biomedical practice. In this paper, our attention is focused on the area of artificial intelligence implementation in photoacoustic spectroscopy of gases. Artificial intelligence has been proven as a very successful, effective, and promising method for the accurate and real-time determination of photoacoustic signal parameters, related to relaxation, thermal and other physical properties of various media (i.e., for solving the inverse photoacoustic problem). To improve the sensitivity and selectivity of the photoacoustic method feedforward multilayer perceptron network is applied for real-time simultaneous determination of photoacoustic signal parameters: vibrational-to-translational relaxation time, and radius of the laser beam. Also, to solve the problem of finding optimal values of these photoacoustic parameters, metaheuristic algorithms, genetic algorithms and simulated annealing are used. The performance of artificial intelligence methods has been tested on a set of experimental signals generated in the (SF6+Ar) gas. The potential advantages and disadvantages of those methods are discussed.

Wavelet-Based Machine Learning Algorithms for Photoacoustic Gas Sensing

“…The paper [24] explores the integration of artificial intelligence (AI) in photoacoustic spectroscopy-a powerful and nondestructive technique with broad applications. The focus lies in utilizing AI to achieve the precise and real-time determination of photoacoustic signal parameters, addressing the inverse photoacoustic problem.…”