Moisture-dependent frictional and aerodynamic properties of safflower seeds

Kara, Mazhar; Bastaban, Saim; Öztürk, İsmail; Kalkan, Fatih; Yildiz, Cihat

doi:10.2478/v10247-012-0029-3

Cited by 12 publications

(14 citation statements)

References 9 publications

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“…The terminal velocity increased with moisture content. Similar results were also reported for safflower seeds, sunflower seeds and coffee cherries/beans (Afonso Junior et al, 2007;Gupta et al, 2007;Kara et al 2012). The increase of terminal velocity with moisture content may have been caused by the increase of seed mass.…”

Section: Determination Of Terminal Velocity Of Faba Bean Seedssupporting

confidence: 77%

Aerodynamic properties of Faba bean (<italic>Vicia faba</italic> L.) Seeds

DİLMAÇ

Tarhan

Polatci

2016

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Section: Determination Of Terminal Velocity Of Faba Bean Seedssupporting

confidence: 77%

Aerodynamic properties of Faba bean (<italic>Vicia faba</italic> L.) Seeds

DİLMAÇ

Tarhan

Polatci

2016

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“…To demonstrate the capability of the proposed network (WNN), a model was developed using data prepared in our laboratory. According to the literature regarding the friction coefficient, the main factors affecting the friction coefficient are moisture content, type of surface, and sliding velocity (Ahmadi et al, ; Altuntaş et al, ; Asli‐Ardeh, Abbaspour‐Gilandeh, & Shojaei, ; Kara et al, ; S. Shafaei & Kamgar, ; S. Shafaei, Nourmohamadi‐Moghadami, & Kamgar, ; S. M. Shafaei et al, ). It is noted that for prediction of SFC, the input data of NNs are the type of surface, moisture content, and sliding velocity with zero value (at the threshold of motion).…”

Section: Methodsmentioning

confidence: 99%

Neural network prediction of friction coefficients of rosemary leaves

Nematollahi

Khaneghah

2019

J Food Process Engineering

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In the current study, the static friction coefficient (SFC) and dynamic friction coefficient (DFC) of rosemary leaves were predicted. In this regard, a wavelet‐based neural network (WNN) was proposed as a tool for mapping of the inputs including moisture content (3.7–23 d. b.%), contact surface (aluminum, rubber, glass, galvanized steel, and plywood), and sliding velocity (1.25–16.5 cm/s) and output data (friction coefficients of rosemary leaves) which were obtained as a result of laboratory activities. Also, 80% and 20% of the total data set were used for training and testing the WNN, respectively, while the statistical parameters for test data set of the proposed NN (MAPE = 6.1310%, RMSE = 0.0468, R2 = 0.956) were compared with those obtained from conventional NNs, such as multilayer perceptron (MLP) (MAPE = 4.1787%, RMSE = 0.0248, R2 = 0.968) and radial basis function (RBF) (MAPE = 10.0311%, RMSE = 0.0591, R2 = 0.831). The results indicated that the accuracy of the MLP NN was slightly better than that of the proposed WNN; however, the learning time of WNN was much less than the MLP NN. The high capability of the proposed NN in the prediction of the SFC and DFC of the rosemary leaves was demonstrated by our findings. Practical applications Data on the frictional characteristics of agricultural products may be useful for the design of specific processing equipment, and laboratory measurements may also provide some interesting results that can guide designers. This study deals with the static friction coefficient (SFC) and dynamic friction coefficient (DFC) prediction of rosemary leaves with a view of performing predictive tools useful in the design of postharvest equipment. Rosemary has been widely cultivated as an aromatic and medicinal plant. The extracted essential oil from rosemary has several applications due to its chemical composition with beneficial properties. The prediction of friction coefficients is performed by a wavelet‐based neural network (WNN). The proposed NN has a multiresolution nature that enables accurate modeling and also fast training. The results showed that the proposed NN is a powerful tool to predict the friction coefficients of rosemary leaves.

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“…When comparing the terminal velocity of yellow lentils with the terminal velocities of vetch seeds, the latter were found to have a higher terminal velocity when moisture levels were the same (Yalcin & Ozarslan 2004). Additionally, safflower seeds were found to have a higher terminal velocity (Kara et al 2012). …”

Section: Bulk Densitymentioning

confidence: 97%

Effects of moisture content on some physical properties of the yellow lentil

Işık

İzli

2016

Tarım Bilimleri Dergisi

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This research focuses on yellow lentil seeds and determining the effects of moisture content on some of their physical properties. The various types of machinery used in gathering crops, processing, shipping, categorizing, sorting, and packaging are designed according to the physical properties of the specific crops. When the moisture content increased from 15.6% to 22.5% d.b., the bulk density decreased from 750.27 to 625 kg m -3 , the true density increased from 1200.5 to 1334 kg m -3, and the rupture force decreased from 2.5 to 16.2 N. Under the same conditions, the measurements of the average thickness, diameter, arithmetic mean diameter, geometric mean diameter, projected area, surface area, sphericity, thousand seed mass, porosity, and terminal velocity values varied as follows: from 1.22 to 1.42 mm, 4.29 to 4.6 mm, 3.26 to 3.53 mm, 2.82 to 3.1 mm, 15.33 to 17.66 mm

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Moisture-dependent frictional and aerodynamic properties of safflower seeds

Cited by 12 publications

References 9 publications

Aerodynamic properties of Faba bean (<italic>Vicia faba</italic> L.) Seeds

Aerodynamic properties of Faba bean (<italic>Vicia faba</italic> L.) Seeds

Neural network prediction of friction coefficients of rosemary leaves

Effects of moisture content on some physical properties of the yellow lentil

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