Capabilities of Machine Learning Methods in Prediction of Solubility of Substances in Supercritical Carbon Dioxide

被引：0
作者：
Lavrukhina, D. A. [1 ]
Pavlov, A. D. [1 ]
Shleimovich, M. P. [1 ]
Bilalov, T. R. [1 ]
机构：
[1] Tupolev Kazan Natl Res Tech Univ KAI, Kazan, Russia
来源：
RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY B | 2024年 / 18卷 / 08期
关键词：
supercritical fluid; solubility; prediction; machine learning; POLYCYCLIC AROMATIC-HYDROCARBONS; SOLID SOLUBILITIES; DRUG SOLUBILITY; NEURAL-NETWORK; FLUID EXTRACTION; MODEL; CO2; PHENANTHRENE; APPARATUS; SOLVENT;
D O I：
10.1134/S1990793124701690
中图分类号：
O64 [物理化学（理论化学）、化学物理学]; O56 [分子物理学、原子物理学];
学科分类号：
070203 ; 070304 ; 081704 ; 1406 ;
摘要：
This work reviews studies of the application of machine learning methods and neural network technologies in the prediction of solubility of various substances in supercritical fluids. By the example of an existing data set on the solubility of aromatic hydrocarbons in supercritical carbon dioxide, a prototype of a solubility prediction system was developed using a simple three-layer neural network. Its efficiency is shown, and further directions of research in this field are identified.
引用
收藏
页码：1815 / 1820
页数：6
相关论文
共 66 条
  • [1] SUPERCRITICAL FLUID EXTRACTION WITH CARBON-DIOXIDE AND ETHYLENE
    ADACHI, Y
    LU, BCY
    [J]. FLUID PHASE EQUILIBRIA, 1983, 14 (OCT) : 147 - 156
  • [2] Ajchariyapagorn A., 2008, American Journal of Food Technology, V3, P275
  • [3] Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models
    Alshahrani, Saad M.
    Al Saqr, Ahmed
    Alfadhel, Munerah M.
    Alshetaili, Abdullah S.
    Almutairy, Bjad K.
    Alsubaiyel, Amal M.
    Almari, Ali H.
    Alamoudi, Jawaher Abdullah
    Abourehab, Mohammed A. S.
    [J]. MOLECULES, 2022, 27 (18):
  • [4] Novel numerical simulation of drug solubility in supercritical CO2 using machine learning technique: Lenalidomide case study
    Alzhrani, Rami M.
    Almalki, Atiah H.
    Alaqel, Saleh L.
    Alshehri, Sameer
    [J]. ARABIAN JOURNAL OF CHEMISTRY, 2022, 15 (11)
  • [5] A generalized neural network model for the VLE of supercritical carbon dioxide fluid extraction of fatty oils
    Aminian, Ali
    ZareNezhad, Bahman
    [J]. FUEL, 2020, 282
  • [6] Estimating the solubility of different solutes in supercritical CO2 covering a wide range of operating conditions by using neural network models
    Aminian, Ali
    [J]. JOURNAL OF SUPERCRITICAL FLUIDS, 2017, 125 : 79 - 87
  • [7] Machine learning model for prediction of drug solubility in supercritical solvent: Modeling and experimental validation
    An, Feifei
    Sayed, Biju Theruvil
    Parra, Rosario Mireya Romero
    Hamad, Mohammed Haider
    Sivaraman, R.
    Foumani, Zahra Zanjani
    Rushchitc, Anastasia Andreevna
    El-Maghawry, Enas
    Alzhrani, Rami M.
    Alshehri, Sameer
    AboRas, Kareem M.
    [J]. JOURNAL OF MOLECULAR LIQUIDS, 2022, 363
  • [8] Solubilities of solids in supercritical fluids .1. New quasistatic experimental method for polycyclic aromatic hydrocarbons (PAHs) plus pure fluids
    Anitescu, G
    Tavlarides, LL
    [J]. JOURNAL OF SUPERCRITICAL FLUIDS, 1997, 10 (03) : 175 - 189
  • [9] Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches
    Baghban, Alireza
    Ahmadi, Mohammad Ali
    Shahraki, Bahram Hashemi
    [J]. JOURNAL OF SUPERCRITICAL FLUIDS, 2015, 98 : 50 - 64
  • [10] Predicting the equilibrium solubility of CO2 in alcohols, ketones, and glycol ethers: Application of ensemble learning and deep learning approaches
    Bahmaninia, Hamid
    Shateri, Mohammadhadi
    Atashrouz, Saeid
    Jabbour, Karam
    Hemmati-Sarapardeh, Abdolhossein
    Mohaddespour, Ahmad
    [J]. FLUID PHASE EQUILIBRIA, 2023, 567
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