Open Access

ARTICLE

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

by Amin Bemani, Alireza Baghban, Shahaboddin Shamshirband, Amir Mosavi, Peter Csiba, Annamaria R. Varkonyi-Koczy

1 Petroleum Engineering Department, Petroleum University of Technology, Ahwaz, Iran.
2 Chemical Engineering Department, Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran.
3 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam.
4 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam.
5 Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, 1034, Hungary.
6 Institute of Structural Mechanics, Bauhaus University Weimar, Weimar, D-99423, Germany.
7 Department of Mathematics and Informatics, J. Selye University, Komarno, 94501, Slovakia.

* Corresponding Author: Shahaboddin Shamshirband. Email: .

Computers, Materials & Continua 2020, 63(3), 1175-1204. https://doi.org/10.32604/cmc.2020.07723

Received 21 June 2019; Accepted 28 July 2019; Issue published 30 April 2020

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. 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.

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