Preprint Article Version 2 This version is not peer-reviewed

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

Version 1 : Received: 6 June 2019 / Approved: 7 June 2019 / Online: 7 June 2019 (12:18:48 CEST)
Version 2 : Received: 30 July 2019 / Approved: 31 July 2019 / Online: 31 July 2019 (04:35:26 CEST)

How to cite: Bemani, A.; Baghban, A.; Shamshirband, S.; Mosavi, A.; Csiba, P.; Várkonyi-Kóczy, A.R. Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2. Preprints 2019, 2019060055 (doi: 10.20944/preprints201906.0055.v2). Bemani, A.; Baghban, A.; Shamshirband, S.; Mosavi, A.; Csiba, P.; Várkonyi-Kóczy, A.R. Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2. Preprints 2019, 2019060055 (doi: 10.20944/preprints201906.0055.v2).

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.

Subject Areas

supercritical carbon dioxide; machine learning modeling; acid; artificial intelligence; solubility; artificial neural networks (ANN); adaptive neuro-fuzzy inference system (ANFIS); least-squares support-vector machine (LSSVM); multi-layer perceptron (MLP); engineering applications of artificial intelligence

Comments (1)

Comment 1
Received: 31 July 2019
Commenter: Shahab Shamshirband
Commenter's Conflict of Interests: Author
Comment: We have extensively revised the paper. Many errors in the figures have been revised. The calculations and modeling have been checked and fixed with the involvement of the new researchers and collaborators. The English and scientific writing errors have been fixed. Citations and mathematical models have been checked and in many parts have been fixed.
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