Сapabilities of machine learning methods in prediction of solubility of substances in supercritical carbon dioxide

2024, №3

сс. 4-12

D.A. Lavrukhina, A.D. Pavlov, M.P. Shleimovich, T.R. Bilalov

Key words: supercritical fluid, solubility, prediction, machine learning

A review of researches devoted to the application of machine learning methods and neural network technologies in the prediction of solubility of various substances in supercritical (SC) fluids. Using a simple neural network of three layers a prototype of a solubility prediction system was developed based on the example of an existing data set on the solubility of aromatic hydrocarbons in SC carbon dioxide. Its efficiency has been shown and further directions of research in this field have been identified.

Full text (PDF)

Translated article

doi:10.1134/S1990793124701690

Полный текст статьи

Следующая статья

Цитировать

ГОСТ
7.0.100-2018

Лаврухина, Д.А. Сapabilities of machine learning methods in prediction of solubility of substances in supercritical carbon dioxide / Д.А. Лаврухина, А.Д. Павлов, М.П. Шлеймович [и др.]. – Текст : // Сверхкритические Флюиды: Теория и Практика.. – 2024. – Т. 19. – № 3. – С. 4-12. - DOI: 10.34984/SCFTP.2024.19.3.001

TRANSLIT

Lavrukhina, D.A. Vozmozhnosti metodov mashinnogo obucheniya v zadachah prognozirovaniya rastvorimosti veshchestv v sverhkriticheskom diokside ugleroda / D.A. Lavrukhina, A.D. Pavlov, M.P. Shleimovich [i dr.]. – Sverhkriticheskie Flyuidy: Teoriya i Praktika.. – 2024. – Т. 19. – № 3. – S. 4-12. - DOI: 10.34984/SCFTP.2024.19.3.001

D.A. Lavrukhina, A.D. Pavlov, M.P. Shleimovich, T.R. Bilalov Сapabilities of machine learning methods in prediction of solubility of substances in supercritical carbon dioxide. Sverhkriticheskie Flyuidy: Teoriya i Praktika, 3, 4-12 (2024). https://doi.org/10.34984/SCFTP.2024.19.3.001

Сapabilities of machine learning methods in prediction of solubility of substances in supercritical carbon dioxide

Цитировать

Спасибо. Ваши данные отправлены