Experimental and ANN modeling of kerosene fuel desulfurization using a manganese oxide-tin oxide catalyst

Abstract

This research pioneered the use of a nanocatalyst composed of manganese oxide (MnO₂) and stannic oxide (SnO₂) to effectively remove dibenzothiophene (DBT) from kerosene fuel through the catalytic oxidative desulfurization (ODS) process, using hydrogen peroxide (H₂O₂) as the oxidant. Impregnating SnO₂ with varying amounts of MnO₂ was used to manufacture the catalyst. The oxidation experiment ran in a batch reactor with varying reaction times and temperatures to determine optimal conditions. High MnO₂ dispersion over SnO₂ was shown by catalyst characterization data. Under optimal operating parameters (catalyst type: 5 % MnO₂/SnO₂, reaction temperature: 75 °C, and reaction duration: 100 min), the results demonstrated a maximum DBT removal efficiency of 82.84 % from kerosene fuel. This research also provides the construction of Artificial Neural Network (ANN) model to simulate the upgrading of kerosene fuel via desulfurization process. There has been a growing trend toward the diversified use of ANN to represent steady state systems in chemical engineering. MATLAB's code was employed for matching the experimental data to the artificial neural network (ANN) model. The resulted data showed significant agreement between the experimental and predicted outcomes, with regression coefficients (R²) of 0.99902, 0.99986, and 0.99961 and mean square errors (MSE) of 0.266, 0.272, and 0.104 for 0 % MnO₂/SnO₂, 1 % MnO₂/SnO₂, and 5 % MnO₂/SnO₂ respectively. This interactive model provided a solid foundation for understanding the novel behavior of the oxidation process.