Work

Modeling and estimation of CO2 capture by porous liquids through machine learning

Year: 2024

Type: article

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 (CO 2 ). 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 CO 2 in various PLs. This work aims to develop machine learning (ML) modeling methods for accurately estimating CO 2 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 CO 2 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 CO 2 solubility in different PLs of complex structures.

Source: Separation and Purification Technology

DOI: 10.1016/j.seppur.2024.130445

Authors: Farid Amirkhani, Amir Dashti, Hossein Abedsoltan, Amir H. Mohammadi, John L. Zhou, Ali Altaee

Institutions: University of Technology Sydney, Missouri University of Science and Technology, University of Toledo, University of KwaZulu-Natal

Language: English

Topic: Phase Equilibria and Thermodynamics

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,790