Journal
AICHE JOURNAL
Volume 66, Issue 2, Pages -Publisher
WILEY
DOI: 10.1002/aic.16833
Keywords
deep-learning algorithm; flow regime transition; phase fraction; quantitative image analysis method; trickle bed
Categories
Funding
- National Natural Science Foundation of China [21808197]
- National Science Fund for Distinguished Young [21525627]
- Science Fund for Creative Research Groups of National Natural Science Foundation of China [61621002]
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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.
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