On flow regime transition in trickle bed: Development of a novel deep‐learning‐assisted image analysis method

An image analysis method was developed based on deep‐learning algorithms to extract phase fractions quantitatively in a rectangular trickle bed, and the average identification error was lower than 5%. Furthermore, the flow regime transition in the trickle bed was studied. In trickle‐to‐pulse flow tr...

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Vydané v:AIChE journal Ročník 66; číslo 2
Hlavní autori: Wang, Chao, Li, Shaoshuo, Yang, Yao, Huang, Zhengliang, Sun, Jingyuan, Wang, Jingdai, Yang, Yongrong, Liao, Zuwei, Jiang, Binbo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 01.02.2020
American Institute of Chemical Engineers
Predmet:
Algorithms
Deep learning
deep‐learning algorithm
Flow
flow regime transition
Image analysis
Image processing
Learning algorithms
Outliers (statistics)
phase fraction
quantitative image analysis method
trickle bed
Trickle beds
ISSN:0001-1541, 1547-5905
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Shrnutí:An image analysis method was developed based on deep‐learning algorithms to extract phase fractions quantitatively in a rectangular trickle bed, and the average identification error was lower than 5%. Furthermore, the flow regime transition in the trickle bed was studied. In trickle‐to‐pulse flow transition, the trickle flow could be further classified into the stable trickle flow and accelerated one. The SD of liquid fractions and the peak width at half‐height of the probability density curve of liquid fractions were close to zero in stable trickle flow, increased rapidly in accelerated trickle flow, and remained approximately constant in pulse flow. In bubble‐to‐pulse flow transition, dispersed bubbles in bubble flow induced the outliers outside the upper boundary of the boxplot of gas fraction, while alternative appearance of gas‐rich zone and liquid‐rich zone in pulse flow induced outliers outside both the upper and lower boundaries of the boxplot of gas fraction.
Bibliografia:Funding information
the National Natural Science Foundation of China, Grant/Award Number: 21808197; the National Science Fund for Distinguished Young, Grant/Award Number: 21525627; the Science Fund for Creative Research Groups of National Natural Science Foundation of China, Grant/Award Number: 61621002
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16833