process model development and optimal kinetics for fuel desulfurization via a novel nano-catalyst

The elimination of dibenzothiophenes (dbt) from kerosene via the catalytic oxidation desulfurization (ods) process was studied using hydrogen peroxide as an oxidant and mno2/sno2 as a catalyst. the oxidation reaction was examined under various operating conditions, including reaction temperatures (30−75oc), batch time (25-100 min), and loading amounts of mno2 (0, 1, and 5%). the catalysts were conducted in this work using the impregnation method to support mno2 nanoparticles on the surface of sno2. mathematical modeling was established by gproms software to predict dbt concentration, and kinetic parameters and compare them with observed results. according to the results of this study, it was observed that the maximum dbt removal obtained was 94% under the best operating conditions. the findings showed that the behavior of dbt removal was enhanced with rising temperature, batch time, and mno2 loading. the optimal process variables of ods of dbt based on the maximum conversion of dbt were estimated by an optimization technique. finally, it can be concluded that the synthesis of nano-catalysts improved the dbt removal and led to the production of ultra-deep desulfurization for eco-friendly kerosene fuel.