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

被引：3

作者：

Wang, Bo ^{[1
]}

Zhou, Han ^{[1
]}

Jing, Shan ^{[1
]}

Zheng, Qiang ^{[1
]}

Lan, Wenjie ^{[2
]}

Li, Shaowei ^{[1
,3
]}

机构：

[1] Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China

[2] China Petr Univ Beijing, State Key Lab Heavy Oil Proc, Beijing 102249, Peoples R China

[3] Tsinghua Univ, State Key Lab Chem Engn, Beijing 100084, Peoples R China

来源：

CHINESE JOURNAL OF CHEMICAL ENGINEERING | 2024年 / 66卷

基金：

中国国家自然科学基金;

关键词：

Artificial neural network; Drop size; Solvent extraction; Pulsed column; Two-phase flow; Hydrodynamics; SUPPORT VECTOR REGRESSION; BUBBLE-COLUMN REACTORS; PLATE EXTRACTION COLUMN; GAS HOLD-UP; SOLVENT-EXTRACTION; MASS-TRANSFER; PULSED DISC; UNIFIED CORRELATIONS; PRESSURE-DROP; LIQUID;

D O I：

10.1016/j.cjche.2023.11.001

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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%. (c) 2023 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.

引用

页码：71 / 83

页数：13

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