Abstract
Carbon dioxide (CO2) capture is a crucial process to mitigate greenhouse gas emissions and reduce anthropogenic impact on climate change. The 3-D model is choosing to capture carbon dioxide from real natural gas (NG) using a mixed matrix membrane (MMM) consisting of polysulfone (PSF) with nanoparticles of covalent organic frameworks (CT-1). In this work, computational fluid dynamics (CFD) estimated the parameters of MMM for CO2 gas separation. Fick's law is utilized of gas transport over a membrane module, whereas the Navier-Stokes equation describes the gas transport in both the feed and permeate domains of the permeation cell. This study involves the estimation of the membrane's properties, including its permeance and diffusion coefficient. The estimation of these parameters was performed by integrating an artificial neural network (ANN) developed in MATLAB R2021a with computational fluid dynamics simulations in COMSOL 6.1. The goal of the parameter prediction module is to minimize the sum of squared errors (SSE) between the experimental and simulated concentrations in the permeate region. For different gas pairs with operating limitations, the calculated parameters for the MMM predict its performance. Additionally, the results showed that operational variables such as concentration of CO2 and feed pressure have a direct impact on gas permeation, although temperature did not show a clear effect. According to the findings, the CFD model demonstrates a deviation of less than 5% from experimental data for the MMM in gas separation.