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

作　　者：Bo Wang Han Zhou Shan Jing Qiang Zheng Wenjie Lan Shaowei Li 

机构地区：[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]State Key Laboratory of Chemical Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Chinese Journal of Chemical Engineering》2024年第2期71-83,共13页中国化学工程学报（英文版）

基　　金：the support of the National Natural Science Foundation of China(22278234,21776151)。

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%.

关 键 词：Artificial neural network Drop size Solvent extraction Pulsed column Two-phase flow HYDRODYNAMICS 

分 类 号：TQ021.1[化学工程] TP183[自动化与计算机技术—控制理论与控制工程]