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

Jadidi, B., Ebrahimi, M., Ein-Mozaffari, F., & Lohi, A. (2024). Analysis of cohesive particles mixing behavior in a twin-paddle blender: DEM and machine learning applications. Particuology, 90, 350-363. https://doi.org/10.1016/j.partic.2023.12.010

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

Behrooz Jadidi, Mohammadreza Ebrahimi, Farhad Ein-Mozaffari^*, Ali Lohi

Department of Chemical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto M5B 2K3, Canada

10.1016/j.partic.2023.12.010

Volume 90, July 2024, Pages 350-363

Received 1 November 2023, Revised 12 December 2023, Accepted 13 December 2023, Available online 28 December 2023, Version of Record 8 February 2024.

E-mail: fmozaffa@torontomu.ca

Highlights

• Examined the blending of two types of cohesive powder in a mixture.

• Validated discrete element method (DEM) model for a lab-scale single-paddle blender with 3.47% relative error.

• Investigated the impact of various operating parameters on mixing quality.

• Highlighted the promise of employing machine learning in analyzing DEM outcomes.

• Introduced an machine learning model for the mixing index with an mean absolute error value of 0.0183.

Abstract

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.

Graphical abstract

Keywords

Machine learning; Granular mixing; Discrete element method; Mixing kinetics and mechanism; Cohesive particles

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