{"title":"小麦粉筛分过程中颗粒分离及筛盲预测","authors":"K. Siliveru, R. Ambrose","doi":"10.13031/trans.14276","DOIUrl":null,"url":null,"abstract":"HighlightsWheat flour cohesion was modeled using the Johnson-Kendall-Roberts (JKR) contact model.The size-based separation was highly influenced by particle size, particle roughness, cohesion, and sieve opening size.Sieve blinding happened at 15.25 and 10.32 s of sieving for hard red winter (HRW) and soft red winter (SRW) wheat flour particles, respectively.Abstract. Sifting or size-based separation of flour particles is an important operation in the wheat milling process. During the separation process, the flour particles often behave as imperfect solids with discontinuous flow and tend to form agglomerates due to interparticle cohesion. Interparticle cohesion in flours is highly dependent on the particle physical and chemical parameters and influences the sieving process. This study presents the development of a discrete element method (DEM) model to predict the size-based separation of wheat flours at 10% and 14% moisture contents (wet basis). DEM models of the size-based separation process were developed using the Hertz-Mindlin contact model. To account for the interparticle cohesion, the Johnson-Kendall-Roberts (JKR) model was coupled with the contact model. The size-based separation of hard red winter (HRW) and soft red winter (SRW) wheat flours was simulated and then validated using lab-scale experiments. Both the modeling and experimental approaches indicated that the percent particle separation was higher in the sieves with larger openings. Particle size, roughness, and cohesion affected the size-based separation in sieves with smaller openings. The model simulation results for the percent mass retained on the screens and the sieve blinding time were comparable with the experimental results. The standard error of prediction (SEP) ranged from 0.13 to 8.27, which indicates that this approach will be useful to predict the size-based separation of cohesive fine particles. The developed model will also be useful to estimate the sieve blinding time during sifting processes. Keywords: Cohesion, Johnson-Kendall-Roberts model, Sifting, Wheat milling.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":"7 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Predicting Particle Separation and Sieve Blinding During Wheat Flour Sifting\",\"authors\":\"K. Siliveru, R. Ambrose\",\"doi\":\"10.13031/trans.14276\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"HighlightsWheat flour cohesion was modeled using the Johnson-Kendall-Roberts (JKR) contact model.The size-based separation was highly influenced by particle size, particle roughness, cohesion, and sieve opening size.Sieve blinding happened at 15.25 and 10.32 s of sieving for hard red winter (HRW) and soft red winter (SRW) wheat flour particles, respectively.Abstract. Sifting or size-based separation of flour particles is an important operation in the wheat milling process. During the separation process, the flour particles often behave as imperfect solids with discontinuous flow and tend to form agglomerates due to interparticle cohesion. Interparticle cohesion in flours is highly dependent on the particle physical and chemical parameters and influences the sieving process. This study presents the development of a discrete element method (DEM) model to predict the size-based separation of wheat flours at 10% and 14% moisture contents (wet basis). DEM models of the size-based separation process were developed using the Hertz-Mindlin contact model. To account for the interparticle cohesion, the Johnson-Kendall-Roberts (JKR) model was coupled with the contact model. The size-based separation of hard red winter (HRW) and soft red winter (SRW) wheat flours was simulated and then validated using lab-scale experiments. Both the modeling and experimental approaches indicated that the percent particle separation was higher in the sieves with larger openings. Particle size, roughness, and cohesion affected the size-based separation in sieves with smaller openings. The model simulation results for the percent mass retained on the screens and the sieve blinding time were comparable with the experimental results. The standard error of prediction (SEP) ranged from 0.13 to 8.27, which indicates that this approach will be useful to predict the size-based separation of cohesive fine particles. The developed model will also be useful to estimate the sieve blinding time during sifting processes. Keywords: Cohesion, Johnson-Kendall-Roberts model, Sifting, Wheat milling.\",\"PeriodicalId\":23120,\"journal\":{\"name\":\"Transactions of the ASABE\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of the ASABE\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.13031/trans.14276\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the ASABE","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.13031/trans.14276","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
Predicting Particle Separation and Sieve Blinding During Wheat Flour Sifting
HighlightsWheat flour cohesion was modeled using the Johnson-Kendall-Roberts (JKR) contact model.The size-based separation was highly influenced by particle size, particle roughness, cohesion, and sieve opening size.Sieve blinding happened at 15.25 and 10.32 s of sieving for hard red winter (HRW) and soft red winter (SRW) wheat flour particles, respectively.Abstract. Sifting or size-based separation of flour particles is an important operation in the wheat milling process. During the separation process, the flour particles often behave as imperfect solids with discontinuous flow and tend to form agglomerates due to interparticle cohesion. Interparticle cohesion in flours is highly dependent on the particle physical and chemical parameters and influences the sieving process. This study presents the development of a discrete element method (DEM) model to predict the size-based separation of wheat flours at 10% and 14% moisture contents (wet basis). DEM models of the size-based separation process were developed using the Hertz-Mindlin contact model. To account for the interparticle cohesion, the Johnson-Kendall-Roberts (JKR) model was coupled with the contact model. The size-based separation of hard red winter (HRW) and soft red winter (SRW) wheat flours was simulated and then validated using lab-scale experiments. Both the modeling and experimental approaches indicated that the percent particle separation was higher in the sieves with larger openings. Particle size, roughness, and cohesion affected the size-based separation in sieves with smaller openings. The model simulation results for the percent mass retained on the screens and the sieve blinding time were comparable with the experimental results. The standard error of prediction (SEP) ranged from 0.13 to 8.27, which indicates that this approach will be useful to predict the size-based separation of cohesive fine particles. The developed model will also be useful to estimate the sieve blinding time during sifting processes. Keywords: Cohesion, Johnson-Kendall-Roberts model, Sifting, Wheat milling.
期刊介绍:
This peer-reviewed journal publishes research that advances the engineering of agricultural, food, and biological systems. Submissions must include original data, analysis or design, or synthesis of existing information; research information for the improvement of education, design, construction, or manufacturing practice; or significant and convincing evidence that confirms and strengthens the findings of others or that revises ideas or challenges accepted theory.