Compressibility and equivalent bulk modulus of shelled corn

Cheng, Xuduo; Zhang, Qiang; Yan, Xiaojie; Shi, Cuixia

doi:10.1016/j.biosystemseng.2015.10.001

Cited by 18 publications

(7 citation statements)

References 6 publications

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“…Kobus et al [23] showed that the airflow resistance through oat grains increased with the external load and loading time. Haque et al [24] and Cheng et al [25] showed that the porosity decreased under vertical pressure, which increased the airflow resistance of the grain bulk and hindered the aeration operation. Yue et al [26] verified that the pore structure in grain bulk is more complex and tortuous after compacting, resulting in higher airflow resistance.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Study of Pressure Drop Characteristics of Soybean Grain under Vertical Pressure

et al. 2022

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The vertical pressure lead to increased airflow resistance through the grain bulk, which affected the efficiency of ventilation and drying. The effects of vertical pressures at 50, 150, and 250 kPa on the pressure drop characteristics of soybeans were studied using experiment and numerical simulation. The random packing and different compression states for soybean packed beds were generated by the Discrete Element Method (DEM). The Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) were coupled to investigate the radial velocity and pressure drop of soybean bulk. The simulation results showed that the radial porosity had an oscillating distribution, and the radial average dimensionless velocity was consistent with the distribution trend of porosity. The increase in vertical pressure causes a decrease in porosity and an increase in local velocity. The PathFinder code was used as a supplementary method to calculate the pore path and pore characterization parameters, and the resistance coefficient term in the Forchheimer equation was determined. The compression of soybeans measured by the experiment mainly occurred within two hours after loading. The pressure drop of soybeans increased with the vertical pressure, with the average pressure drop at vertical pressures of 50, 150, and 250 kPa being 36%, 57%, and 92% higher than the uncompressed state (0 kPa). The pressure drop of soybeans calculated by the DEM-CFD method and the Forchheimer equation under different vertical pressures were in close agreement with the experimental results, and an average relative difference was found to be less than 10%. These results provide guidance for estimating the pressure drop of soybeans at different grain depths.

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Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Study of Pressure Drop Characteristics of Soybean Grain under Vertical Pressure

et al. 2022

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“…Increases in the impurity contents between the grains allow higher densification and the formation of different compaction layers, resulting in different static pressures between grain points and layers (Cheng et al, 2015). This interferes with the movement and uneven distribution of the intergranular air, which is insufflated by the drying exhaust, possibly causing excessive drying at some points and non-drying of the grains at others.…”

Section: Introductionmentioning

confidence: 99%

Stationary rice drying: Influence of initial moisture contents and impurities in the mass grains on the physicochemical and morphological rice quality

Nunes

Coradi

Müller

et al. 2022

Food Processing Preservation

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The initial moisture contents and impurities in the grain mass can interfere with the drying operation in a stationary dryer. Thus, the objective of this study was to evaluate the effect of drying air movement in the rice mass as a function of initial moisture contents (19, 18, 17, and 16% w.b.) and impurities (1.5, 2.5, 2.0, and 3.0%) on the physicochemical properties and morphological rice quality. The moisture and impurity contents provided alterations in the movement and distribution of the drying air in the upper layers of the stationary dryers. Consequently, the physicochemical properties and morphological quality of rice were altered. In addition, the reduction in the starch and fat contents was correlated to higher percentages of crude protein, mainly upper grain layers. Thus, the rice mass at moisture content of 17% (w.b.) and impurity at 2.5% allowed major uniformity and greater drying versatility in the stationary dryer for rice quality. Practical applications The rice drying in the stationary dryer is an excellent option to reduce drying and storage infrastructure and achieve satisfactory grain quality, as long as there is an adequate control and management of the batches of grain received in terms of moisture uniformity and impurity contents. For proper rice management in the stationary dryer, it is recommended to pre‐cleaning the grain mass to 2% of impurities, while the initial grain moisture content for drying must be below 18% (w.b.). The standardization of batches in terms of initial moisture content and impurities allows for an adequate flow and distribution of air in the grain mass to obtain efficiency in drying at low temperatures and maintaining grain quality. From a commercial point of view, the grains can remain in the drying process and stored in silo‐dryers for the harvest period until the time they can be sold at better market prices.

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“…Corn is one of the largest crops grown around the world due to its application in food, feed, chemical engineering, and energy [1][2][3]. Mechanization and automation for corn harvest have been fully developed in the last century.…”

Section: Introductionmentioning

confidence: 99%

Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

Yuan

Zhao

et al. 2020

Applied Sciences

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Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.

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Compressibility and equivalent bulk modulus of shelled corn

Cited by 18 publications

References 6 publications

Experimental and Numerical Study of Pressure Drop Characteristics of Soybean Grain under Vertical Pressure

Experimental and Numerical Study of Pressure Drop Characteristics of Soybean Grain under Vertical Pressure

Stationary rice drying: Influence of initial moisture contents and impurities in the mass grains on the physicochemical and morphological rice quality

Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

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