A Model of Catalytic Cracking: Product Distribution and Catalyst Deactivation Depending on Saturates, Aromatics and Resins Content in Feed

The problems of catalyst deactivation and optimization of the mixed feedstock become more relevant when the residues are involved as a catalytic cracking feedstock. Through numerical and experimental studies of catalytic cracking, we optimized the composition of the mixed feedstock in order to minimize the catalyst deactivation by coke. A pure vacuum gasoil increases the yields of the wet gas and the gasoline (56.1 and 24.9 wt%). An increase in the ratio of residues up to 50% reduces the gasoline yield due to the catalyst deactivation by 19.9%. However, this provides a rise in the RON of gasoline and the light gasoil yield by 1.9 units and 1.7 wt% Moreover, the ratio of residue may be less than 50%, since the conversion is limited by the regenerator coke burning ability.

CFD Analysis of BED Textural Characteristics on TBR Behavior: Hydrodynamics and Scaling-up

In recent years, CFD has played an important role in the understanding and design of TBR’s. In this work, through CFD with Eulerian approach, a three-phase heterogeneous reactor model was developed, were the accuracy of Interfacial Momentum Exchange Model (IMEM) for the gas-solid interaction, the effect of a more detailed catalytic bed geometry description, and the pellet shape over TBR hydrodynamics of two fluid phases interacting with the solid phase was studied. Then, a second model was developed, where the validated hydrodynamic model was coupled with mass transport for an HDS process of light gasoil. Additionally, in order to insight into the scaling up process of a TBRs, the proposed columns behaviors were compared against literature columns using four different ways, and it was found that the best predictions were obtained when the models’ holdup were equaled to those evaluated in literature columns. Since in reliable literature deviations in pressure drop predictions of around 30% can be found, the model results show significant improvement against literature, achieving 5 times better accuracy in predicting pressure drops, and 50% improvement in holdup prediction; the coupled model reproduces the same conversion values compared with literature data, and predicts conversions with 95% accuracy