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.
Recommended Citation
F. Amirkhani et al., "Modeling and Estimation of CO2 Capture by Porous Liquids through Machine Learning," Separation and Purification Technology, vol. 359, article no. 130445, Elsevier, Jun 2025.
The definitive version is available at https://doi.org/10.1016/j.seppur.2024.130445
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
Included in
Chemical Engineering Commons, Engineering Education Commons, Engineering Science and Materials Commons, Materials Science and Engineering Commons, Risk Analysis Commons