Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Doctor of Philosophy


Chemical and Biochemical Engineering


Zhu, Jesse

2nd Supervisor

Zhang, Chao

Joint Supervisor


This study numerically investigated the reaction behaviours of catalytic ozone decomposition reaction in a 10.2-meter-tall gas-solids circulating fluidized bed (CFB) reactor. A pseudo-homogeneous reactive transport model for ozone decomposition, integrated with the two-fluid model, was developed and validated using experimental data. Based on the model, the impacts of turbulence models, specularity coefficients, and simulation methods on reaction behaviours in the CFB riser reactor were explored. These three factors were found to significantly affect the hydrodynamic characteristics and the reaction behaviours in the riser. A comparative study of CFB riser and downer reactors was conducted. Operations in the direction of and against gravity resulted in drastically different flow fields and particle clustering in the two reactors. More uniform flow and reaction fields make the downer have better gas-solids contact efficiency than the riser. Flow structure and residence time distributions of gas and solids in the riser and downer were characterized by tracing the gas and particles. The results showed that flow in the downer reactor resembles plug flow, while significant axial backmixing occurs in the riser. An internal circulation mechanism is proposed to explain the backmixing. A sub-grid reactive transport model was developed using a filtering method and an artificial neural network (ANN) to explore the impact of particle clustering on reaction behaviours. In the development of the filtered model, employing gradient features as inputs enhanced regression accuracy. Additionally, ANN demonstrated superior performance over traditional fitting methods. Consequently, the filtered reactive transport model showed improvements in predicting the reaction behaviours in a CFB riser. In summary, the hydrodynamic characteristics within CFB predominantly influence reaction behaviours. High-resolution simulations combined with machine learning techniques effectively aid in understanding mechanisms in fluidization system and developing new models, which are crucial for designing and optimizing large-scale reactors.

Summary for Lay Audience

In this work, the reaction behaviours in a gas-solids circulating fluidized bed (GSCFB) reactor were investigated using the computational fluid dynamics (CFD) approach. GSCFB is a widely used reactor in process industries, capable of continuously processing granular materials. By introducing gas, particles are suspended and behave like a fluid. Due to the significant density difference between gas and particles, particle clustering occurs. Particle clustering, a type of meso-scale structure and heterogeneous flow structure, is a typical characteristic of flow in GSCFB, significantly affecting the flow field in the reactor. In the GSCFB, heterogeneous reactions typically occur, with these reactions taking place when reactants in the gas phase come into contact with solid catalysts. Consequently, the hydrodynamic characteristics significantly impact reaction behaviours in the GSCFB. Within the scope of this thesis, these hydrodynamic characteristics are defined by both time-averaged and instantaneous flow fields, and are characterized by gas-solids segregation and clustering. The reaction behaviours, in turn, represent the reaction field, such as the reactant concentration and the reaction rate. The CFD approach, which solves a series of governing equations, has the ability to capture the flow field and reaction field at each moment. This approach has proven effective for both reactor design and theoretical research. In this study, a CFD model for GSCFB reactors was developed. The impact of various factors, including reactor type, turbulence model, and operating conditions, on the hydrodynamic characteristics and reaction behaviours were explored. Additionally, a sub-grid reactive transport model was established to introduce the effects of particle clustering on the reaction behaviours. This model notably improves the accuracy of reaction field predictions by correcting deviations caused by particle clustering. The development of GSCFB relies on both experimental and numerical works. Experiments provide fundamental understanding and reliable data for reactors. Numerical work offers an abundance of reactor data under various types and conditions, aiding in exploring new applications for reactors and shortening development cycles. Additionally, the ample micro- and meso-scale information provided by numerical work helps researchers better understand and investigate complex phenomena in reactors.