Prof. Angelo Earvin Sy Choi
Department of Chemical Engineering, De La Salle UniversitySpeech Title: Multi-Objective Fuzzy Optimization of Ultrasound-Assisted Oxidative Desulfurization for Dibenzothiophene
Abstract: Global environmental regulations require transportation fuels to contain less than 10 ppm sulfur. Dibenzothiophene (DBT) remains one of the most difficult sulfur compounds to remove using conventional hydrodesulfurization (HDS). Ultrasound-assisted oxidative desulfurization (UAOD) offers an alternative under mild operating conditions. Conventional response surface methodology (RSM) focuses on maximizing oxidation efficiency and does not consider operating cost. This study developed a multi-objective fuzzy optimization (MOFO) framework integrated with the ε-constraint method to optimize DBT oxidation efficiency and operating cost simultaneously. The optimization combined the quadratic RSM model of Chen et al. (2025) with an operating cost model. It included catalyst, phase-transfer agent, oxidant, and electrical energy consumption. The Pareto frontier showed that DBT conversion ranged from 45.98 % to 100 %, while operating cost ranged from 7.28 to 13.65 USD/L. The MOFO model identified a fuzzy optimum with a satisfaction degree of 0.7886 at an initial sulfur concentration of 291.55 ppm, a catalyst dosage of 0.11 g, a reaction time of 25.63 min, and an H2O2/DBT volumetric ratio of 0.92. Under these conditions, the model achieved 88.58 % DBT oxidation at an operating cost of 8.63 USD/L. Compared with complete conversion, the fuzzy optimum reduced operating cost by 36.8 % while sacrificing only 11.42 % oxidation efficiency. Relative to the single-objective RSM optimum, the proposed MOFO framework also reduced catalyst loading, reaction time, and oxidant consumption while maintaining high desulfurization performance. These results demonstrate that MOFO provides a practical approach for balancing oxidation efficiency and operating cost and can support the economic optimization of UAOD processes.