Computational quest for effective sorbents for the desulfurization of transportation fuels

Abstract

Petroleum remains the most widely utilized primary energy source in the United States; however, its combustion releases hazardous sulfur oxides (SOx) due to the inherent presence of sulfur-containing compounds, leading to significant environmental and human health impacts, including acid rain, respiratory issues, and reduced crop yields. Moreover, sulfur compounds hinder the performance of modern vehicle emission control systems and poison electrode catalysts in fuel cell technologies, necessitating sulfur concentrations below 10 ppm or even trace levels. Current desulfurization methods are inadequate for achieving ultra-low sulfur content, highlighting the need for innovative, cost-effective solutions. This study explores the use of computational simulation, specifically Grand Canonical Monte Carlo (GCMC) simulations, to systematically screen zeolite materials as sorbents for removal of pollutant sulfur compounds. The results established correlations between zeolite properties and their adsorption performance, offering insights into sorbent behavior and selectivity. This systematic screening paves the way for further functionalization of high-potential zeolites to achieve ultra-deep desulfurization. By advancing cost-effective sorbent design, this study contributes to improving air quality, safeguarding human health and the environment, enhancing fuel quality, and meeting stringent regulatory standards for transportation fuels.