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In the present work, a novel and the robust computational investigation iscarried out to estimate solubility of different acids in supercritical carbondioxide. Four different algorithms such as radial basis function artificialneural network, Multi-layer Perceptron (MLP) artificial neural network (ANN),Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inferencesystem (ANFIS) are developed to predict the solubility of different acids incarbon dioxide based on the temperature, pressure, hydrogen number, carbonnumber, molecular weight, and acid dissociation constant of acid. In thepurpose of best evaluation of proposed models, different graphical andstatistical analyses and also a novel sensitivity analysis are carried out. Thepresent study proposed the great manners for best acid solubility estimation insupercritical carbon dioxide, which can be helpful for engineers and chemiststo predict operational conditions in industries.

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO$_2$