Using an encoder-decoder convolutional neural network to predict the solid holdup patterns in a pseudo-2d fluidized bed

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2021-12-31

Department

Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

7

Series

Chemical Engineering Science, Volume 246

Abstract

In this paper, the capability of combined use of computational fluid dynamics (CFD) and data-based deep learning to predict fluidized beds' complex behavior without solving transport equations is being examined. A convolutional neural network (CNN) is trained to anticipate fluidized bed volume fraction contours based on the numerical simulations' results and data-based machine learning. The trained CNN receives the first ten frames from the CFD as input and predicts the next frame. This process continues until all the required frames are obtained. The results show CNN's superior spatial learning capability and how its combination with CFD can reduce the required computational power without compromising accuracy.

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Keywords

CFD, Deep learning, Fluidized bed, convolutional neural networks

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DOI

10.1016/j.ces.2021.116886

Citation

Bazai, H, Kargar, E & Mehrabi, M 2021, ' Using an encoder-decoder convolutional neural network to predict the solid holdup patterns in a pseudo-2d fluidized bed ', Chemical Engineering Science, vol. 246, 116886 . https://doi.org/10.1016/j.ces.2021.116886

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202107017842

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[article-cris] Sähkötekniikan korkeakoulu / ELEC