Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors
Author:
Affiliation:
1. Department of Chemical Engineering, Indian Institute of Technology, Jammu, Jammu & Kashmir 181221, India
Funder
Department of Science and Technology, Ministry of Science and Technology
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00669
Reference67 articles.
1. Review of (gas)-liquid-solid circulating fluidized beds as biochemical and environmental reactors
2. CFD analysis of hydrodynamic, heat transfer and reaction of three phase riser reactor
3. Xanthan production in bubble column and air-lift reactors
4. Design and scale-up of the Fischer–Tropsch bubble column slurry reactor
5. Scale-up strategy for bubble column slurry reactors using CFD simulations
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1. Computational Fluid Dynamics (CFD)- Deep Neural Network (DNN) Model to Predict Hydrodynamic Parameters in Rectangular and Cylindrical Bubble Columns;Digital Chemical Engineering;2024-09
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