The conversion of CO2 gas from the global emission to methanol can be a route to look at in addressing greenhouse gas (GHG) issues. Photocatalysis has been attracting attention in the conversion of CO2 to methanol, as it is seen to be one of the most viable, economic, and sustainable strategies. The biggest hindrance to the use of metal oxide photocatalysts was the poisoning by sulfur content in the CO2 gas feedstock. Therefore, in the development of photocatalysts using metal oxide-based additives, the metal needs to be in the form of metal sulfides to avoid catalyst poisoning due to the presence of H2S. The magnesium sulfide-based TiO2 (MgS-TiO2) photocatalyst has not been synthesized and studied for its photocatalytic potential. In this study, a novel MgS-TiO2 photocatalyst was synthesized using a combination of wet impregnation and hydrothermal method and characterized to determine the physical and chemical properties of the photocatalyst. Characterization results have shown the presence of MgS on the native TiO2 photocatalyst. The optimization of MgS-TiO2 formulation was conducted, wherein the MgS and TiO2 ratio of 0.5 wt % has been shown to give the highest methanol yield of 229.1 μmol/g·h. The photocatalytic parameter optimization results showed that temperature and catalyst loading were the most important factors that impacted the photocatalytic process. In contrast, reaction time had the least significant effect on the CO2 photocatalytic reduction to methanol. This concludes that the MgS-TiO2 photocatalyst has potential and can be used for the photocatalytic reduction of CO2 to methanol.
The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.
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