Abstract: At present, many countries are becoming more and more stringent in terms of sulfur content in fuel oil. S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number. To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry, a first-principle model of S Zorb was established based on industry data. In order to describe the desulfurization and the other side reactions, a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm. Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively. The results indicate that the hybrid models can predict the mass fraction of PIONA, sulfur content and research octane number of the refined gasoline accurately, of which the mean absolute percentage errors are less than 6%. Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss. Optimization results show that the average reduction of the loss of research octane number is 21.8%, which suggests that the models developed hold promise for guiding practical production.

Key words: Desulfurization, Simulation, Reaction kinetics, Optimization

摘要： At present, many countries are becoming more and more stringent in terms of sulfur content in fuel oil. S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number. To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry, a first-principle model of S Zorb was established based on industry data. In order to describe the desulfurization and the other side reactions, a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm. Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively. The results indicate that the hybrid models can predict the mass fraction of PIONA, sulfur content and research octane number of the refined gasoline accurately, of which the mean absolute percentage errors are less than 6%. Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss. Optimization results show that the average reduction of the loss of research octane number is 21.8%, which suggests that the models developed hold promise for guiding practical production.

关键词: Desulfurization, Simulation, Reaction kinetics, Optimization