Open AccessDetermining temperature and partial pressures of the components of a high-temperature gas mixture using artificial neural networks
Author(s) -
D. E. Kashirskiĭ
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1680/1/012016
Subject(s) - artificial neural network , inverse , carbon dioxide , oxide , approximation error , inverse problem , nitrogen , water vapor , component (thermodynamics) , materials science , partial pressure , biological system , thermodynamics , computer science , chemistry , mathematics , artificial intelligence , algorithm , oxygen , meteorology , physics , mathematical analysis , organic chemistry , geometry , biology , metallurgy
The article deals with solving the inverse problem of gaseous media optics by determining the parameters of high-temperature gaseous media from its transmittances using the artificial neural networks. The study of the dependence of the maximum relative error in determining the desired parameters on the size of the training set and the artificial neural network configuration is carried out. The possibility of solving the inverse problem in the case of a four-component gas mixture (water vapor, carbon dioxide, carbon oxide and nitrogen oxide) is shown.