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

TL;DR

This study compares four computational models to accurately predict acid solubility in supercritical CO2, aiding industrial process optimization through advanced machine learning techniques.

Contribution

It introduces a comprehensive evaluation of ANN, ANFIS, and LSSVM models for acid solubility prediction in supercritical CO2, including a novel sensitivity analysis.

Findings

LSSVM outperforms other models in accuracy.

Sensitivity analysis identifies key parameters affecting solubility.

Models effectively predict solubility based on multiple molecular and operational variables.

Abstract

In the present work, a novel and the robust computational investigation is carried out to estimate 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. In 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 the 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.