## 5 Conclusions  
The current state of computational fluid dynamics-discrete phase method (CFD-DPM) simulations in process engineering is marked by significant advancements and a growing range of applications. The method demonstrates its potential in various engineering fields, including transportation, erosion, spray, comminution, separation, mixing, tracking and thermochemical conversion. It provides valuable insights into the complex interactions between fluid and discrete phases, leading to improved process optimization, design, product quality control, safety and risk assessment, and energy efficiency.  
The integration of CFD-DPM with other numerical methods and the development of novel Euler-Lagrange techniques have enhanced the computational efficiency and maintained the discrete characteristics of solid phases, making it suitable for industrial-scale applications. The use of coarsening technology in the simulations has been a key factor in speeding up the simulation process, which is crucial for handling large-scale equipment and complex systems. However, there are still challenges to overcome. The high computational requirements for long-time simulations and the need for more accurate models for complex multiphase flows are areas that require further development. The current models may not fully capture the intricacies of certain processes, such as the detailed behavior of particles in a fluidized bed or the complex reactions in thermochemical processes.  
The future of CFD-DPM is promising, with continuous improvements in computing performance and the emergence of hybrid computing strategies that combine graphics processing units (GPUs) with central processing units (CPUs). These advancements will enable more complex simulations with higher spatial and temporal resolution. Additionally, the integration of machine learning algorithms with CFD-DPM holds great potential for addressing the challenges of real-time optimization and control, as well as for enhancing the predictive capabilities of the simulations. While CFD-DPM has made significant strides in process engineering, there is still room for growth and refinement. The future outlook is positive, with the potential for even more widespread application and the development of more sophisticated models that can accurately represent the complex dynamics of multiphase systems.  
**Author Contributions:** X.Y.: Writing--original draft, investigation; T.X.: data curation, conceptualization, methodology; Y.Q.: methodology, visualization; H.Z.: investigation, data curation; Y.W.: supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key Research and Development Program of China under the title of \"Research and Development of Intelligent and Efficient Pollination of Hybrid Rice Seed Production and Pollination UAV Technology and Equipment for Mountain Rice\" (Grant number: 2023 YFD2000403). We thank the National Natural Science Foundation of China for the financial support of our project \"Study on kinematics and dynamics of pollen and pneumatic pollination method in hybrid rice seed production\" (Grant number: 31971796). We also greatly appreciate the support of the Zhejiang Province San Nong Jiu Fang Science and Technology Cooperation Program project \"Research and Development of Hybrid Rice Seed Production and Pollination Machine\" (Grant number: 2023S NJF048). The research is also supported by the earmarked fund for \"China Agriculture Research System\" (CARS--01).  
The support of the National Key Laboratory of Agricultural Equipment Technology is greatly appreciated.  
The authors declare no conflict of interest.