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Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models

文献类型：期刊论文

作者Su, Zhenning3,4; Wang, Yong1,2,3,4; Tan, Boren3,4; Cheng, Quanzhong3,4; Duan, Xiaofei1,2; Xu, Dongbing3,4; Tian, Liangliang5; Qi, Tao3,4
刊名CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION
出版日期2023
卷号183页码:11
ISSN号0255-2701
关键词Pulsed disk and doughnut column Machine learning Modeling Feature importance
DOI10.1016/j.cep.2022.109248
英文摘要Pulsed disk and doughnut column (PDDC) is widely applied in liquid-liquid solvent extraction. Due to a nonlinear and complex mechanism in PDDC, existing single empirical models often fail to predict the performance of different PDDCs. In this work, machine learning (ML) models such as random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are developed to predict the PDDC's performance including dispersed-phase holdup (xd), drop size (d32), axial diffusion coefficient (Ec) and the height of mass transfer unit (Hoc). ML models were trained based on a comprehensive dataset and the results showed that the prediction performances of the ML models are better than the empirical correlations. The best average absolute relative error (AARE) and correlation coefficient (R2) of d32, xd, Ec and Hoc were 3.97% and 0.99, 10.16% and 0.955, 12.71% and 0.973, 13.44% and 0.982, respectively. RF and SVM exhibited the highest predictive accuracy. Furthermore, the feature importance was determined, which indicated the most significant features for d32, xd, Ec and Hoc were pulse intensity, the velocity of dispersed phase, the velocity of continuous phase and the properties of continuous phase, respectively. This study provided a new perspective to model and design PDDC.
WOS关键词DISPERSED-PHASE HOLDUP ; NON-PULSED DISC ; AXIAL-DISPERSION ; DROP SIZE ; MASS-TRANSFER ; CAPROLACTAM ; TOLUENE
资助项目National Natural Science Foundation of China[92262305] ; National Natural Science Foundation of China[22178350] ; CAS Project for Young Scientists in Basic Research[YSBR-038] ; Major scientific and technological achievements transformation project of Hebei Province[22293601Z] ; Cooperation Project between Chongqing universities and the Chinese Academy of Sciences[HZ2021013]
WOS研究方向Energy & Fuels ; Engineering
语种英语
出版者ELSEVIER SCIENCE SA
WOS记录号WOS:000904453400004
资助机构National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; Major scientific and technological achievements transformation project of Hebei Province ; Cooperation Project between Chongqing universities and the Chinese Academy of Sciences
源URL[http://ir.ipe.ac.cn/handle/122111/56829]  
通讯作者Wang, Yong
作者单位1.Univ Melbourne, Melbourne TrACEES Platform, Melbourne 3010, Australia
2.Univ Melbourne, Sch Chem, Melbourne 3010, Australia
3.Univ Chinese Acad Sci, Beijing 101400, Peoples R China
4.Chinese Acad Sci, Inst Proc Engn, Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
5.Chongqing Univ Arts & Sci, Sch Elect Informat & Elect Engn, Chongqing 400000, Peoples R China
推荐引用方式
GB/T 7714		 Su, Zhenning,Wang, Yong,Tan, Boren,et al. Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models[J]. CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION,2023,183:11.
APA Su, Zhenning.,Wang, Yong.,Tan, Boren.,Cheng, Quanzhong.,Duan, Xiaofei.,...&Qi, Tao.(2023).Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models.CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION,183,11.
MLA Su, Zhenning,et al."Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models".CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 183(2023):11.

入库方式： OAI收割

来源：过程工程研究所

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