https://scholars.lib.ntu.edu.tw/handle/123456789/635660
標題: | Machine Learning for Two-Phase Flow Separation in a Liquid-Liquid Interface Manipulation Separator | 作者: | Chang, Yi Chieh CHEN, Y. J. Chen, Po Ying Chen, Yu Chieh Maqbool, Faisal Ho, Tsung Yi YA-YU CHIANG |
關鍵字: | classifiers | machine learning | majority-voting algorithm | multilayer perceptron | phase separation | 公開日期: | 8-三月-2023 | 卷: | 15 | 期: | 9 | 來源出版物: | ACS Applied Materials and Interfaces | 摘要: | Two-phase flow separation is a key step in various downstream purification processes. The use of a separator with controllable flow behavior is recommended to avoid contamination. In this study, a core-annular separator for biphasic flow separation with four different chemical polarities was developed, and two machine learning-based methods were proposed for answering two emergent questions to meet real industrial needs. (1) Could complete two-phase separation be achieved under these operating conditions? (2) Could the separation process be accelerated by determining the maximum input flow rate of the water? Process prediction for automation, machine learning-based classifiers, and multilayer perceptron were used to address these questions by predicting successful separation and the maximum input flow rates of unknown water-solvent systems with limited experimental data as training samples. The core-annular separator achieved complete two-phase water-solvent separation at a maximum total input flow rate of 4000 μL min-1. Moreover, the classification accuracy for complete separation reached 92.2%, and the multilayer perceptron network had the best performance for predicting the flow rate. This liquid-liquid interface manipulation separator and machine learning method could decrease the cost of relevant process development. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/635660 | ISSN: | 19448244 | DOI: | 10.1021/acsami.2c17291 |
顯示於: | 機械工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。