Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

EasyChair Preprint 2294

Abstract

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

Keyphrases: ACID, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Intelligence, Artificial Neural Networks (ANN), ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, Least Squares Support Vector Machine (LSSVM), Multi-Layer Perceptron (MLP), machine learning, solubility, supercritical carbon dioxide

@booklet{EasyChair:2294,
  author    = {Amin Bemani and Alireza Baghban and Shahaboddin Shamshirband and Amir Mosavi and Peter Csiba and Annamária R. Várkonyi-Kóczy},
  title     = {Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2},
  howpublished = {EasyChair Preprint 2294},
  year      = {EasyChair, 2020}}