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• W4313397772 endingPage "118185" @default.
• W4313397772 startingPage "118185" @default.
• W4313397772 abstract "The quick prediction of the separation efficiency of gas cyclones is key to the rapid analysis of geometrical factors and designs. The computational Fluid Dynamics (CFD)-based Multiphase Particle-in-Cell (MP-PIC) model is used to describe internal solid behaviours under a range of conditions. An integrated simulation database for the cyclone design with six geometrical factors, including vortex finder diameter, inlet height, volute height, cylinder height, cone height, and cone tip diameter, is created. Two predictive models, including the Genetic Algorithm-Back Propagation Neural Network (GA-BP) Model and the Response Methodology Model (RSM), are compared. The CFD results show that the velocities of gas flow in the central slice show distinct ranges and distributions. The prediction results show that compared with the RSM model, the GA-BP model is more accurate in the prediction of separation efficiency. This paper presents a cost-effective tool to predict the separation efficiency of cyclones in practical industries." @default.
• W4313397772 created "2023-01-06" @default.
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• W4313397772 date "2023-02-01" @default.
• W4313397772 modified "2023-10-09" @default.
• W4313397772 title "Prediction of separation efficiency in gas cyclones based on RSM and GA-BP: Effect of geometry designs" @default.
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• W4313397772 doi "https://doi.org/10.1016/j.powtec.2022.118185" @default.
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