Muhammad Saad Khan

To use renewable energy and mitigate the greenhouse gas effect on the environment, carbon dioxide (CO₂) separation is crucial. Recent research has shown that using membranes for CO₂ separation is a vital option, but challenges remain with permeability and selectivity. Blend hollow fiber membranes offer a solution to these challenges. In this study, a blend of hollow fiber membranes made of polyether sulfone (PES) and polyvinyl acetate (PVAc) was developed, along with the incorporation of fillers, to overcome the permselectivity challenge. This resulted in the creation of mixed matrix blend hollow fiber membranes by using the phase inversion method. The membranes that were developed were examined using FESEM, FTIR, XRD, TGA, and pure-gas permeation analysis. The polymer blend demonstrated miscibility and preserved strong morphological and structural stability during CO₂ and CH₄ separation tests, exhibiting little deformation and reliable performance across different feed pressures ranging from 2 to 8 bar. The addition of TiO₂ improved the compatibility of the polymer blend and increased the mobility of CO₂. At 8 bar, the optimized PES/PVAc–5 wt.% TiO₂ hollow fiber membrane reached a CO₂ permeance of 92.22 GPU, representing an enhancement of about 24.7% in CO₂/CH₄ selectivity compared to the standard membrane. This emphasizes its potential for effective biogas upgrading under high pressure.

Gas hydrates are reservoirs of CO2, CH4, and other gases, found in seabed subsurface sediments, in permafrost regions and in deep freshwater lakes. These gases can be transported using hydrate technologies. However, when gas is transported through oil and gas pipelines, the formation of gas hydrates inside the pipelines causes blockage of the pipelines, damages the pipelines, and sometimes causes loss of life. Several hydrate mitigation methods exist, and chemical inhibition has gained significant attention. Ammonium-based ionic liquids have received considerable attention as chemical inhibitors because of their eco-friendliness, low volatility, and reusability. However, these experimental methods are time-consuming and expensive. Hence, machine learning model building is the best and most suitable complement to experimental work for gas hydrate inhibition. A support vector machine (SVM) learning model was built with and without hyperparameter optimization for CO2/CH4 gas hydrate inhibition using ammonium-based ionic liquids (AILs). The data were collected from the literature and thermodynamic models. Outliers were removed from the data. The models were trained and tested using a 70:30 ratio. The model performance was analyzed using experimental versus predicted temperature, residual analysis, cumulative probability plot, and error analysis metrics and unseen data. It was concluded that SVM model hyperparameter optimization is a necessary for CO2/CH4 hydrate inhibition using AILs.