Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm-Artificial Neural Network Modeling,Chemical Engineering & Technology

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Minimum Fluidization Velocities of Binary Solid Mixtures: Empirical Correlation and Genetic Algorithm-Artificial Neural Network Modeling
Chemical Engineering & Technology ( IF 1.8 ) Pub Date : 2021-11-02 , DOI: 10.1002/ceat.202100170
Sudipta Let ₁ , Nirjhar Bar _{1,

2} , Ranjan Kumar Basu ₁ , Sudip Kumar Das ₁

Affiliation

Experimental investigation of the fluidization behavior in single and binary solid-liquid fluidized beds of nonspherical particles as solid phase and water as liquid phase was performed in a Perspex column. Different particle sizes were used to prepare single and binary mixtures with different weight ratios for fluidization. Minimum fluidization velocity increased with increasing average particle size and decreasing sphericity for the binary mixture. An empirical correlation was developed to predict the minimum fluidization velocity. Genetic algorithm-artificial neural network (GA-ANN) modeling was applied to predict the minimum fluidization velocity for single and binary solid-liquid fluidized beds. The application of GA-ANN analysis leads to designing binary solid-liquid fluidization systems without experimentation.

中文翻译：

二元固体混合物的最小流化速度：经验相关性和遗传算法-人工神经网络建模

在有机玻璃柱中对非球形颗粒为固相和水为液相的单一和二元固液流化床中的流化行为进行了实验研究。使用不同的粒径来制备具有不同重量比的用于流化的单一和二元混合物。对于二元混合物，最小流化速度随着平均粒径的增加和球形度的降低而增加。开发了经验相关性来预测最小流化速度。应用遗传算法-人工神经网络 (GA-ANN) 建模来预测单一和二元固液流化床的最小流化速度。GA-ANN 分析的应用导致无需实验即可设计二元固液流化系统。

更新日期：2021-12-16

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