Analysis of cohesive particles mixing behavior in a twin-paddle blender:DEM and machine learning applications  

作　　者：Behrooz Jadidi Mohammadreza Ebrahimi Farhad Ein-Mozaffari Ali Lohi 

机构地区：[1]Department of Chemical Engineering,Toronto Metropolitan University,350 Victoria Street,Toronto M5B 2K3,Canada

出　　处：《Particuology》2024年第7期350-363,共14页颗粒学报（英文版）

基　　金：the Natural Sciences and Engineering Research Council of Canada(grant No.RGPIN-2019-04644)is gratefully acknowledged.

摘　　要：This research paper presents a comprehensive discrete element method(DEM)examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender.A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%,demonstrating a strong agreement between the results from the experimental tests and the DEM simulation.The main focus centers on systematically exploring how operational parameters,such as impeller rotational speed,blender's fill level,and particle mass ratio,influence the process.The investigation also illustrates the significant influence of the mixing time on the mixing quality.To gain a deeper understanding of the DEM simulation findings,an analytical tool called multivariate polynomial regression in machine learning is employed.This method uncovers significant connections between the DEM results and the operational parameters,providing a more comprehensive insight into their interrelationships.The multivariate polynomial regression model exhibited robust predictive performance,with a mean absolute percentage error of less than 3%for both the training and validation sets,indicating a slight deviation from actual values.The model's precision was confirmed by low mean absolute error values of 0.0144(80%of the dataset in the training set)and 0.0183(20%of the dataset in the validation set).The study offers valuable insights into granular mixing behaviors,with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.

关 键 词：Machine learning Granular mixing Discrete element method Mixing kinetics and mechanism Cohesive particles 

分 类 号：O17[理学—数学]