Data science integrated with computational fluid dynamics for particle collision modeling in fluidized bed
1
Issued Date
2024-04-01
Resource Type
ISSN
09218831
eISSN
15685527
Scopus ID
2-s2.0-85189682801
Journal Title
Advanced Powder Technology
Volume
35
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Advanced Powder Technology Vol.35 No.4 (2024)
Suggested Citation
Nimmanterdwong P., Yurata T., Chaiprasitpol N., Pranomsri N., Chalermsinsuwan B. Data science integrated with computational fluid dynamics for particle collision modeling in fluidized bed. Advanced Powder Technology Vol.35 No.4 (2024). doi:10.1016/j.apt.2024.104419 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/97957
Title
Data science integrated with computational fluid dynamics for particle collision modeling in fluidized bed
Corresponding Author(s)
Other Contributor(s)
Abstract
Fluidized beds are critical in numerous industrial applications, including chemical processing and energy production. Accurate modeling of solid particles and fluids within these beds is essential for process optimization and improved efficiency. In this study, we developed a machine learning model to predict the coefficient of restitution (COR) of solid particles in fluidized beds. Four variables—collision velocity, effective temperature, effective mass, and effective elastic modulus—were used in the model. Our dataset comprised 2446 data points from previous literature. We employed self-organizing map (SOM) and artificial neural network (ANN) approaches for data analysis. The initial ANN model, which did not incorporate data clustering, exhibited an impressive coefficient of determination (R-squared) of 0.989, indicating its high accuracy. To enhance the model further, we clustered the data into four groups using SOM and developed separate ANN models for each group. All four models achieved R-squared greater than 0.99, illustrating the effectiveness of data clustering. The resulting model can be integrated into simulation software, such as MFIX, to provide a more precise representation of fluidized bed behavior across various industrial settings. The findings emphasize the potential of machine learning models to enhance fluidized bed simulations, leading to increased efficiency and cost-effectiveness in industrial processes. Future studies should explore the inclusion of additional variables and extend the model's application to different industrial processes. Additionally, incorporating recommendations for optimizing fluidized bed behavior based on the model's predictions would provide valuable insights for process engineers.