We built a molecular-level kinetic model for hydrocarbon catalytic cracking, incorporating the catalyst acidity as the parameter to estimate the reaction rates. The n-decane and 1-hexene co-conversion catalytic cracking process was chosen as the studying case. The reaction network was automatically generated with a computer-aided algorithm. A modified linear free energy relationship was proposed to estimate the activation energy in a complex reaction system. The kinetic parameters were initially regressed from the experimental data under various reaction conditions. On this basis, the product composition was evaluated for three catalytic cracking catalysts with different Si/Al. The Bronsted acid and Lewis acid as the key catalyst properties were correlated with the kinetic parameters. The built model can calculate the product distribution, and molecular composition at different reaction conditions for different catalysts. The sensitive study shows that it will facilitate the model-based optimization of catalysts and reaction conditions according to product demands.

This work aims to develop a molecular-level process model framework for simulating the FCC process. The process model consists of the riser, regenerator, and separation models. A complex molecular-level kinetic model, containing 3,652 molecules and 8,202 reactions, was developed for the heavy oil FCC process. The kinetic model was coupled with the riser model, and the model parameter was tuned by a set of systematic experimental data from a pilot-scale plant. After that, a two-zone and two-phase regenerator model was built. The regenerator was combined with the riser model, and the coupled modeling and process simulation for the riser-type FCC unit was developed. The results show that the calculated value of fraction yields and key bulk properties agrees well with the experimental data. Moreover, the molecular distribution of the product and temperature profile was also predicted.

The molecular conversion of complex mixture involves a large number of species and reactions. The corresponding kinetic model is consist of a series of ordinary differential equations (ODEs) with severe stiffness, leading to an exponentially growing computational time. To reduce the computational time, we proposed a mass-temperature decoupled discretization strategy for a large-scale molecular-level kinetic model. The method separates the mass balance and heat balance calculations in the rigorous adiabatic reactor model and divided the reactor into several isothermal segments. After discretization, the differential equations for heat balance can be replaced by algebraic equations between nodes. We used a molecular-level diesel hydrotreating kinetic model as the case to validate the proposed method. We investigated the effects of temperature estimation methods and node number on the accuracy of the model. A good agreement between the discretization model and rigorous model was observed while the computational time was significantly shortened