TY - EJOU
AU - Bemani, Amin
AU - Baghban, Alireza
AU - Shamshirband, Shahaboddin
AU - Mosavi, Amir
AU - Csiba, Peter
AU - Varkonyi-Koczy, Annamaria R.
TI - Applying ANN, ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO_{2}
T2 - Computers, Materials \& Continua
PY - 2020
VL - 63
IS - 3
SN - 1546-2226
AB - 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.
KW - Supercritical carbon dioxide
KW - machine learning
KW - acid
KW - artificial intelligence
KW - solubility
KW - artificial neural networks (ANN)
KW - adaptive neuro-fuzzy inference system (ANFIS)
KW - least-squares support vector machine (LSSVM)
KW - multilayer perceptron (MLP)
DO - 10.32604/cmc.2020.07723