"Modeling and Estimation of CO2 Capture by Porous Liquids through Machi" by Farid Amirkhani, Amir Dashti et al.
 

Abstract

Porous liquids (PLs) are newly developed porous materials that combine unique fluidity with permanent porosity, which exhibit promising functionalities. They have shown ability to efficiently absorb greenhouse gases such as carbon dioxide (CO2). Experimental measurement is one approach to determining the solubility of various greenhouse gases in PLs, which has drawbacks such as being expensive and time-consuming. Hence, simulation models are valuable to predict the solubility of CO2 in various PLs. This work aims to develop machine learning (ML) modeling methods for accurately estimating CO2 solubility under varying conditions (e.g. PLs, temperature, pressure). Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), Coupled Simulated Annealing-Least Squares Support Vector Machine (CSA-LSSVM), and Multilayer Perceptron Neural Network (MLP-NN) were established as the state of art algorithms for estimating CO2 solubility. The models demonstrated accurate modeling results with average absolute relative deviation (AARD) of 12.98%, 8.67%, 3.17% and 6.64% for ANFIS, PSO-ANFIS, CSA-LSSVM and MLP-NN, respectively. This work has presented a powerful modeling tool with few parameters that need to be controlled, to precisely estimate CO2 solubility in different PLs of complex structures.

Department(s)

Chemical and Biochemical Engineering

Publication Status

Open Access

Keywords and Phrases

CO absorption 2; Greenhouse gas; Machine learning; Modeling; Porous liquids

International Standard Serial Number (ISSN)

1873-3794; 1383-5866

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Elsevier, All rights reserved.

Publication Date

22 Jun 2025

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