[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120114-en":3,"doc-seo-120114-105":30,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120114,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Simulation of granular flows and machine learning in food processing","Granular materials appear throughout food processing, yet their flow behavior and underlying movement mechanisms remain insufficiently understood. This work presents chute granular-flow modeling and simulation using both discrete element method (DEM) and continuum approaches. Simulation outputs are then used to train machine-learning models, including Random Forest, Linear Regression, and Ridge Regression, to predict granular flow patterns. Results from DEM and continuum show strong agreement in representing chute flow, while the machine-learning strategy demonstrates promising predictive potential for more complex flow conditions.","Simulation of granular flows and machine learning in food processing.  \nCUI, X, ADEBAYO, D, ZHANG, Hongwei \u003C [http://orcid.org/0000-0002-7718-](http://orcid.org/0000-0002-7718-)[ ](http://orcid.org/0000-0002-7718-)021X>, HOWARTH, Martin, ANDERSON, A, OLOPADE, T, SALAMI, K and FAROOQ, S  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/34594/](https://shura.shu.ac.uk/34594/)  \nThis document is the Published Version [VoR]  \nCitation:  \nCUI, X, ADEBAYO, D, ZHANG, Hongwei, HOWARTH, Martin, ANDERSON, A, OLOPADE, T, SALAMI, K and FAROOQ, S (2024) . Simulation of granular flows and machine learning in food processing. Frontiers in Food Science and Technology, 4.[Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nTYPE Original Research PUBLISHED 12 December 2024 DOI 10.3389/frfst.2024.1491396  \nOPEN ACCESS  \nEDITED BY  \nNikolai I. Lebovka,  \nNational Academy of Sciences of Ukraine, Ukraine  \nREVIEWED BY  \nRoberto Arevalo,  \nResearch Centre For Energy Resources And Consumption, Spain  \nCarlos Manuel Carlevaro,  \nNational Scientific and Technical Research Council (CONICET), Argentina  \nSimulation of granular flows and machine learning in food processing  \nX. Cui 􀀁 1*, D. Adebayo 1, H. Zhang 2, M. Howarth 2, A. Anderson 3, T. Olopade 1, K. Salami 1 and S. Farooq 1  \n1School of Computing, Engineering & Digital Technologies Teesside University, Middlesbrough, United Kingdom, 2National Centre of Excellence for Food Engineering, Sheffield, United Kingdom, 3Koolmill Systems Ltd., Shirley, United Kingdom  \n*CORRESPONDENCE  \nX. Cui,  \n [x.cui@tees.ac.uk](x.cui@tees.ac.uk)  \nRECEIVED 04 September 2024  \nACCEPTED 14 November 2024  \nPUBLISHED 12 December 2024  \nCITATION  \nCui X, Adebayo D, Zhang H, Howarth M, Anderson A, Olopade T, Salami K and Farooq S (2024) Simulation of granular flows and machine learning in food processing.  \nFront. Food. Sci. Technol. 4:1491396 .  \ndoi: 10.3389/frfst.2024.1491396  \nCOPYRIGHT  \n© 2024 Cui, Adebayo, Zhang, Howarth, Anderson, Olopade, Salami and Farooq. This isan 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) and the copyright owner(s) 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.  \nGranular 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.  \nKEYWORDS  \ngranular flows, DEM, continuum, machine learning, random forest, food processing PACS  \n1 Introduction  \nGranular materials are among the most commonly encountered media in natural and industrial processes, perhaps second o","cbCaiir2DzWYYjRY","https://ap.wps.com/l/cbCaiir2DzWYYjRY","pdf",4026278,1,12,"English","en",105,"# Introduction\n## Granular materials in natural and industrial processes\n## Motivation and knowledge gaps in granular-flow modeling\n## Challenges from particle interactions and intrinsic properties","[{\"question\":\"Why are the results important for practical food-processing problems?\",\"answer\":\"The chute-flow results can serve as a benchmark for tackling more complex granular-flow scenarios influenced by particle shape, size, interparticle interactions, and external obstacles.\"}]","Simulation of granular flows and machine learning in food processing | PDF",1785728286,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"simulation-of-granular-flows-and-machine-learning-in-food-processing","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/simulation-of-granular-flows-and-machine-learning-in-food-processing/120114/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Why are the results important for practical food-processing problems?","Question",{"text":76,"@type":77},"The chute-flow results can serve as a benchmark for tackling more complex granular-flow scenarios influenced by particle shape, size, interparticle interactions, and external obstacles.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,108,113,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":114},"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":99,"slug":130},19,"General","general"]