@Article{cmc.2020.07723,
AUTHOR = {Amin Bemani, Alireza Baghban, Shahaboddin Shamshirband, Amir Mosavi, Peter Csiba, Annamaria R. Varkonyi-Koczy},
TITLE = {Applying ANN, ANFIS and LSSVM Models for Estimation of  Acid Solvent Solubility in Supercritical CO<sub>2</sub>},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1175--1204},
URL = {http://www.techscience.com/cmc/v63n3/38869},
ISSN = {1546-2226},
ABSTRACT = {In the present work, a novel machine learning computational investigation is 
carried out to accurately predict the solubility of different acids in supercritical carbon 
dioxide. Four different machine learning algorithms of radial basis function, multi-layer 
perceptron (MLP), artificial neural networks (ANN), least squares support vector machine 
(LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the 
solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen 
number, carbon number, molecular weight, and the dissociation constant of acid. To 
evaluate the proposed models, different graphical and statistical analyses, along with novel 
sensitivity analysis, are carried out. The present study proposes an efficient tool for acid 
solubility estimation in supercritical carbon dioxide, which can be highly beneficial for 
engineers and chemists to predict operational conditions in industries.},
DOI = {10.32604/cmc.2020.07723}
}