Diffusion model for thin layer drying process of corn

Hacıhafızoğlu, Oktay; Cihan, Ahmet; Kahveci, Kamil; Korkmaz, C

doi:10.1243/09544089jpme253

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Among the tested models, the diffusion approach model was the most suitable for expressing the moisture ratio at different temperatures with almost the lowest MSE and highest R-square. The diffusion approach was also used for describing the drying process of pomelo (Yildiz & İzli, 2019) and corn (Hacihafizoğlu et al, 2009). Another study on tarragon also showed that the diffusion approach model gave the best fit to predict drying behavior (Arabhosseini et al, 2009).…”

Section: Applying Mathematical Modelmentioning

confidence: 99%

Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice

Loan

Thủy

Tai

2023

Food Sci. Technol (Campinas)

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Modeling moisture content variation under variable hot air dryers is challenging. In this study, mathematical models and artificial neural network (ANN) were investigated for modeling of instant “Cẩm” brown rice drying process. The experiments were done in four levels of hot air temperature (55, 60, 65, and 70 °C). The results demonstrated that among eight mathematical models, the diffusion approach could give the best prediction of moisture ratio during the drying process with the highest R-square and lowest mean square error. Besides, the ANN model with 10 hidden layers also could provide the best-fit model with the same criteria as the mathematical model. Compared with the ANN model, both can give a highly accurate prediction. However, the ANN model could be more beneficial in the up-scale process.

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Section: Applying Mathematical Modelmentioning

confidence: 99%

Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice

Loan

Thủy

Tai

2023

Food Sci. Technol (Campinas)

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“…The moisture content of foodstuff should be reduced in order to prevent deterioration during storage after harvesting. The moisture content of the corn after harvesting is around 24-25% (dry basis) and must be reduced below 14% (dry basis) by a suitable drying process [1].…”

Section: Introductionmentioning

confidence: 99%

“…Doymaz and Pala [5] used empirical exponential model and Page model for describing the thin layer drying process of corn and found that the Page model is better than the exponential model. Hacihafizoglu et al [1] used a liquid diffusion model for the simulation of continuous drying of corn with an initial mois-THERMAL SCIENCE: Year 2019, Vol. 23,No.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of intermittent drying of a corn for different drying conditions

Hacıhafızoğlu¹,

Susantez²,

Kahveci³

et al. 2018

THERM SCI

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In this study, intermittent drying process of corn was studied numerically for various intermittent periods and drying air temperatures. An Arrhenius type diffusicoefficient D = e (-b/T) ⋅ 10-9 m 2 /s was proposed for the moisture diffusion inside the corn. Numerical simulations were performed by choosing the suitable value for drying constant, b, that yields the best agreement with experimental drying rates. The experimental results were obtained via an experimental setup for intermittent periods of 30 minute and 60 minute, and drying air temperatures of 40 °C, 50 °C, 60 °C, and 70 °C. The results show that overall agreement between the experimental and theoretical prediction is good. On the other hand, the theoretical results overestimate the moisture ratio at the initial stage and underestimate it at the later stage of drying.

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“…11 In many theoretical studies on convective drying modeling, the temperature of the material during the drying process was assumed to be constant at drying air temperature and the effect of the drying air temperature was considered in the diffusion coefficient. [12][13][14][15][16][17][18][19] This assumption results in a higher drying rate due to the higher diffusion coefficient.…”

mentioning

confidence: 99%

“…The first is a Dirichlet-type boundary condition and the material surface is assumed to be at equilibrium moisture content. This assumption, which has been used in many drying models, [12][13][14][15][16][17][18][19] causes higher mass transfer rates at the material surface. The second type of boundary condition, which is more realistic, is a Neumann-type boundary condition, and it has also been used in several studies.…”

mentioning

confidence: 99%

Modeling and numerical simulation of simultaneous heat and mass transfer during convective drying of porous materials

Kahveci

2016

Textile Research Journal

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In this study, a model was developed and numerical simulations based on the developed model were performed to investigate the convective air drying behavior of porous materials and to discuss the reliability of assumptions often used in convective drying models. Simulation results were obtained for a wool yarn drying process with various values of the drying air temperature and inlet velocity. The results show that a symmetric pair of vortices is formed in the wake region of the material as a result of boundary separation. The length of the vortex formation increases with the inlet velocity, depending on the increase in the Reynolds number. The results also show that temperature is the major factor affecting the drying rate, while the velocity has a secondary effect. The results also show that the temperature in the material and on the surface of the material varies significantly depending on the evaporation rate, developing boundary layer, and boundary layer separation. Therefore, a constant temperature assumption in the material and on the material surface is not reasonable. Furthermore, the results show that the temperature gets significantly lower values, especially in the early stages of drying, due to the energy consumed for evaporation. Therefore, it is not reasonable to neglect evaporation, as this would result in an over-prediction of the drying rate.

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Diffusion model for thin layer drying process of corn

Cited by 4 publications

References 19 publications

Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice

Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice

Numerical investigation of intermittent drying of a corn for different drying conditions

Modeling and numerical simulation of simultaneous heat and mass transfer during convective drying of porous materials

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