Optimized split-flow deflector design for airflow drying systems using BP neural network coupled with NSGA-II algorithm

Wu, X. and Yu, D. and Zhu, Y. and Xiao, Y. and Liu, Y. and Mao, S. and Du, Y. (2026) Optimized split-flow deflector design for airflow drying systems using BP neural network coupled with NSGA-II algorithm. Energy, 352: 140948. ISSN 0360-5442

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Abstract

The non-uniform distribution of hot airflow and the high intensity of turbulence critically affect the effectiveness of airflow drying systems. In this study, numerical simulations were conducted to analyze the internal flow field characteristics of an airflow dryer. A novel split-flow deflector plate structure was designed and optimized using Response Surface Methodology (RSM) integrated with a backpropagation (BP) neural network and the NSGA-II algorithm. The results indicated that the designed split-flow deflector structure demonstrated superior performance compared with conventional arc-shaped designs. The optimized performance was achieved with a rotation speed of 190 r/min, deflector plate angle of 92°, and split-flow plate length of 380 mm. Compared to both the arc-shaped and pre-optimized configurations, the optimized split-flow deflector increased average airflow velocity by 4% and 39.2% and improved the airflow uniformity index by 11.8% and 14.5%, respectively. Furthermore, transient thermal analysis confirmed enhanced thermal stability as the outlet temperature rapidly converged to the experimental benchmark (160 °C), effectively mitigating thermal oscillations and intensifying gas-solid heat exchange. The findings provide both theoretical insight into the dynamic control of particle flows in dryers and a practical basis for the structural optimization of industrial airflow drying equipment.