A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

Bo Wang ¹Han Zhou ¹Shan Jing ¹Qiang Zheng ¹Wenjie Lan ²Shaowei Li³

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作者信息

1. Institute of Nuclear and New Energy Technology,Tsinghua University,Beijing 100084,China
2. State Key Laboratory of Heavy Oil Processing,China University of Petroleum(Beijing),Beijing 102249,China
3. Institute of Nuclear and New Energy Technology,Tsinghua University,Beijing 100084,China;State Key Laboratory of Chemical Engineering,Tsinghua University,Beijing 100084,China
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Abstract

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8％and 9.3％,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6％and 18.5％and the deviations in correlations are 11％and 15％.

Key words

Artificial neural network/Drop size/Solvent extraction/Pulsed column/Two-phase flow/Hydrodynamics

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基金项目

National Natural Science Foundation of China(22278234)

National Natural Science Foundation of China(21776151)

出版年

2024

中国化学工程学报(英文版)

中国化工学会

中国化学工程学报(英文版)

CSTPCDEI

影响因子：0.818

ISSN：1004-9541

参考文献量68

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