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ORIGINAL RESEARCH article

Front. Food. Sci. Technol.
Sec. Food Modeling
Volume 4 - 2024 | doi: 10.3389/frfst.2024.1491396
This article is part of the Research Topic Advancing Food Processing: Novel Technologies and Modeling Techniques View all 3 articles

Simulation of granular flows and machine learning in food processing

Provisionally accepted
Xinjun Cui Xinjun Cui 1,2*Damilola Adebayo Damilola Adebayo 2Hongwei Zhang Hongwei Zhang 3Martin Howarth Martin Howarth 3Alec Anderson Alec Anderson 4Taiwo Olopade Taiwo Olopade 2Kelvin Salami Kelvin Salami 2Salman Farooq Salman Farooq 2
  • 1 Teesside University, Middlesbrough, United Kingdom
  • 2 School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom
  • 3 National Centre of Excellence for Food Engineering, Industry and Innovation Research Institute, Sheffield Hallam University, Sheffield, England, United Kingdom
  • 4 Koolmill Systems Ltd, 11 Stoneford Road, Shirley, Solihull B90 2EA, Solihull, United Kingdom

The final, formatted version of the article will be published soon.

    Granular materials are widely encountered in food processing, but understanding their behavior and movement mechanisms remains in the early stages of research. In this paper, we present our recent modeling and simulation work on chute granular flow using both the discrete element method (DEM) and continuum method. Based on the simulation data, we apply machine learning techniques such as Random Forest, Linear Regression, and Ridge Regression to evaluate the effectiveness of these models in predicting granular flow patterns. The granular materials in our study consist of soft-sphere particles with a 1 mm diameter, driven by gravity as they flow down a chute inclined relative to the horizontal plane. Our DEM and continuum simulation results show good agreement in modeling the chute flow, and the machine learning approach demonstrates promising potential for predicting flow patterns. The results of this chute flow study can provide a benchmark solution for more complex flow problems involving factors such as particle shape, size, interparticle interactions, and external obstacles.

    Keywords: granular flows, DEM, continuum, machine learning, random forest, food processing PACS:

    Received: 04 Sep 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Cui, Adebayo, Zhang, Howarth, Anderson, Olopade, Salami and Farooq. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Xinjun Cui, Teesside University, Middlesbrough, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.