Abstract
Particle coating process, one of the main methods to improve the particle properties, is widely used in industrial production and pharmaceutical industry. For the scale up and optimization of this process, a mechanistic and detailed study is needed or numerical simulation as an alternative way. Decomposition of substances usually involves multiple chemical reactions and produces multiple substances in the actual chemical reaction. In the study, a chemical reaction flow (CRF) model has been established based on kinetic mechanism of elementary reaction, the theory of molecular thermodynamics and the sweep theory. It was established with the comprehensive consideration of the decomposition of substances, deposition process, adhesion process, desorption process, hydrogen inhibition, and clearance effect. Then the CFD-DEM model was coupled with CRF model to simulate particle coating process by FB-CVD method, and the CFD-DEM-CRF coupling model was implemented in the software Fluent-EDEM with their user definition function (UDF) and application programming interface (API). The coating process in the spouted bed was analyzed in detail and the coating behavior under different conditions were compared at the aspects of CVD rate, coating efficiency, particle concentration distribution, particle mixing index and gas concentration distribution. It is found that the average CVD rate is 6.06 × 10−4 mg/s when the inlet gas velocity is 11 m/s and bed temperature is 1273 K, and simulation result agrees with the experimental result well. Average CVD rate and coating efficiency increase with temperature increasing, but decrease acutely with mass fraction of injected hydrogen increasing. The CFD-DEM-CRF coupling model can be developed as a basic model for investigating particle coating process in detail and depth and can provide some guidance for the operating conditions and parameters design of the spouted bed in the real coating process.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: Grant Nos: 91634113, 21306097
Funding source: The National Major Science and Technology Projects of China
Award Identifier / Grant number: No. ZX06901
Acknowledgments
The authors would like to thank the National Natural Science Foundation of China (Grant Nos: 91634113, 21306097) and the National Major Science and Technology Projects of China (No. ZX06901) for the financial support provided.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This research was supported by the National Natural Science Foundation of China (Grant Nos: 91634113, 21306097) and the National Major Science and Technology Projects of China (No. ZX06901).
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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