Predicting Particle Separation and Sieve Blinding During Wheat Flour Sifting

IF 1.4 4区 农林科学 Q3 AGRICULTURAL ENGINEERING
K. Siliveru, R. Ambrose
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引用次数: 1

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.
小麦粉筛分过程中颗粒分离及筛盲预测
使用Johnson-Kendall-Roberts (JKR)接触模型对小麦粉的粘聚力进行了建模。颗粒粒度、颗粒粗糙度、黏结力和筛孔尺寸对粒度分离有较大影响。硬红冬(HRW)和软红冬(SRW)面粉颗粒分别在筛分15.25和10.32 s时发生筛盲。面粉颗粒的筛分或基于粒度的分离是小麦制粉过程中的重要操作。在分离过程中,面粉颗粒往往表现为不连续流动的不完全固体,由于颗粒间的粘聚,容易形成结块。面粉中颗粒间的粘聚性高度依赖于颗粒的物理和化学参数,并影响筛分过程。本研究提出了一种离散元法(DEM)模型的发展,以预测小麦面粉在10%和14%含水量(湿基)下基于粒度的分离。采用Hertz-Mindlin接触模型建立了基于尺寸的分离过程的DEM模型。为了解释粒子间的内聚力,将Johnson-Kendall-Roberts (JKR)模型与接触模型相结合。对基于粒度的硬红冬小麦(HRW)和软红冬小麦(SRW)面粉分离进行了模拟,并通过实验室规模实验进行了验证。模型和实验方法均表明,孔径越大的筛子,颗粒分离率越高。粒径、粗糙度和黏结力影响孔径较小的筛子中基于粒径的分离。模型模拟结果与实验结果吻合较好。预测的标准误差(SEP)范围为0.13 ~ 8.27,表明该方法可用于预测黏结细颗粒基于尺寸的分离。该模型还可用于筛分过程中筛盲时间的估计。关键词:粘聚力,Johnson-Kendall-Roberts模型,筛分,小麦碾磨
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来源期刊
Transactions of the ASABE
Transactions of the ASABE AGRICULTURAL ENGINEERING-
CiteScore
2.30
自引率
0.00%
发文量
0
审稿时长
6 months
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.
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