Removal of sulfur compounds from sour gas using metal nano-absorbents: Process optimization

• A novel hybrid process combining carbon nanotube nanofluids and an applied magnetic field was developed for sour gas sweetening. • The magnetic field significantly improved mass transfer rate (↑3.6 %) and mass transfer coefficient (↑4.8 %) by enhancing nanoparticle mobility and inducing localized heating. • The use of packed bed reactors and stable nanofluids allowed for effective hydrogen sulfide removal under varying porosity, temperature, and gas concentration conditions. • Response Surface Methodology (RSM) was employed to optimize four operational parameters, achieving a high model validity (R² = 0.9972). • The method demonstrates potential for lower energy consumption, selective adsorption , and modular scalability , especially for high-H₂S gas streams. Sour gas, a natural gas laden with significant quantities of the highly toxic and corrosive hydrogen sulfide (H2S), presents substantial environmental and economic challenges due to its sulfur content. This study explores the potential of nanotechnology to enhance hydrogen sulfide removal from sour gas. The corrosive nature of sulfur compounds necessitates substantial investments in equipment maintenance and protection, particularly within gas pipelines and storage facilities. The removal or reduction of sulfur compounds, including H2S, associated with oil and gas operations offers significant economic, environmental, and health benefits. A hybrid adsorption and magnetic field approach was employed to capture H2S from natural sour gas. The application of a magnetic field elevated the operating temperature, influencing the mobility of carbon nanotubes within the nanofluid and the nanofluid layer. Consequently, a current of 1.5 amperes resulted in a 4.8 % increase in mass transfer coefficient and a 3.6 % increase in mass flow rate. Based on the results of the analysis of variance (ANOVA), as well as the comparison of predicted and experimental data and residual plots, the degree of validity of the predicted model is considered satisfactory. This research demonstrates that the R² coefficient is 0.9972 and the Adjusted R² coefficient is 0.9948.