Amin Bemani1, Alireza Baghban2, Shahaboddin Shamshirband3, 4, *, Amir Mosavi5, 6, 7, Peter Csiba7, Annamaria R. Varkonyi-Koczy5, 7
CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1175-1204, 2020, DOI:10.32604/cmc.2020.07723
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.… More >
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