Coupling Artificial Neural Network with EMMS drag for simulation of dense fluidized beds. (31st December 2021)

• Record Type: Journal Article
• Authors: Yang, Zhuo; Lu, Bona; Wang, Wei
• Is Part Of: Chemical engineering science. Volume 246(2021)
• Publication Date: 2021-12-31
• Subjects: EMMS -- Drag coefficient -- Artificial neural network -- Fluidized bed -- CFD simulation; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.117003
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: A generic EMMS drag model for dense fluidization is developed by using ANN. The contribution of different variables to the drag correction is quantified. The model precision and simulation cost are balanced to determine ANN parameters. Five fluidized beds with different conditions are predicted well with the new drag. Abstract: The previous sub-grid, energy-minimization multi-scale (EMMS) drag models were all established at certain fixed operating conditions and material properties. In this study, we developed a generic EMMS drag for simulating dense fluidized beds by using the Artificial Neural Network (ANN) to cover a wide range of operating conditions and material properties. To this end, the algorithm of the EMMS model was optimized to provide a huge dataset efficiently and the performance of ANN was tested by training with different numbers of data and hidden layer structures. The EMMS-ANN model was determined by balancing the training precision and computational time and then applied to the simulation of five fluidized beds under different operating conditions and material properties. It was found that the simulation with the EMMS-ANN drag enables reasonable prediction and shows good applicability to a wide range of dense fluidization.