• Journal of Biosystems Engineering
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• v.27 no.1
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• pp.45-50
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• 2002

This study was performed to develop thin layer drying equations fur short grain rough rice using far-infrared ray. Thin layer drying tests was conducted at four far-infrared ray temperature levels of 30, 40, 50, 60$^{\circ}C$ and two initial moisture content levels of 20.7, 26.2%(w.b.). The measured moisture ratios were fitted to Lewis and Page drying models by stepwise multiple regression analysis. Half response time of drying was affected by both drying temperature and initial moisture content at drying temperature of below 40$^{\circ}C$, but at above 40$^{\circ}C$ was mainly affected by drying temperature. Experimental constant(k) in Lewis model was a function of drying temperature, but K and N in Page model were function of drying temperature and initial moisture content. Moisture ratios predicted by two drying models agreed well with experimental values. But in the actual range of drying temperature above 30$^{\circ}C$ Page model was more suitable for predicting of drying rates.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.232-239
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• 2016

Purpose: The aims of this study were to define the drying characteristics of rapeseed and to determine the optimum thin-layer drying model for rapeseed by considering the effects of drying temperature and relative humidity. Methods: The thin-layer drying experiments were conducted at different combinations of drying air temperature levels of 40, 50, and $60^{\circ}C$ and relative humidity levels of 30, 45, and 60%, on both of which drying rate depends. The drying rate increased with increasing air temperature as well as decreasing relative humidity. The 13 models were fitted to the experimental data. Results: From the results of the regression analysis for empirical constants of the Page model, the values of $R^2$ were the highest (ranging from 0.9924 to 0.9966) and the values of RMSE were the lowest (ranging from 0.0169 to 0.0296). Conclusions: For all drying conditions considered, the Page model was determined to be the most suitable model for describing the thin-layer drying of rapeseed (P-value < 0.01). The moisture diffusion coefficients were calculated using the moisture diffusion equation for a spherical shape, based on Fick's second law.

• Journal of Biosystems Engineering
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• v.22 no.1
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• pp.11-20
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• 1997

Thin layer drying tests of short gain rough rice were conducted in an experimental dryer equiped with air conditioning unit. The drying tests were performed in triplicate at three air temperatures of $35^circ$, $45^circ$, $55^circ$, and three relative humidities of 40%, 55%, 70%, respectively. Previously published thin layer equations were reviewed and four different models widely used as thin layer drying equations for cereal grains were selected. The selected four models were Pages, simplified diffusion, Lewis's and Thompson's models. Experimental data were fitted to these equations using stepwise multiple regression analysis. The experimental constants involved in tow equations were represented as a function of temperature and relative humidity of drying air. The results of comparing coefficients of determination and root mean square errors of miosture ratio for low equations showed that Page's and Thompsons models were found to fit adequately to all drying test data with coefficient of determination of 0.99 or better and root mean square error of moisture ratio of 0.025.

• Journal of Biosystems Engineering
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• v.12 no.2
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• pp.38-43
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• 1987

An experimental study was conducted to develop a thin-layer rewetting equation of short grain rough rice of Akihikari variety. Four thin-layer rewetting equations were experimentally determined from $25^{\circ}C$ to $45^{\circ}C$ and 70%RH to 85%RH conditions. Diffusion, Henderson, Page, and Thompson equations widely used as thin-layer drying equations were selected. Experimental data were fitted to these equations using linear regression analysis except diffusion equation. The diffusivity in the diffusion equation was determined by optimization method. Four equations were highly significant. In order to compare the goodness of fit of each equation, the error mean square of each equawas calculated. The diffusion model was not a very good model because the error mean square was very large. The other three models showed the same level or error mean square and could predict satisfactorily the rewetting rate or short grain rough rice.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.190-198
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• 2017

Purpose: A thin-layer drying equation was developed to analyze the drying processes of soybeans (white and black beans) and investigate drying conditions by verifying the suitability of existing grain drying equations. Methods: The drying rates of domestic soybeans were measured in a drying experiment using air at a constant temperature and humidity. The drying rate of soybeans was measured at two temperatures, 50 and $60^{\circ}C$, and three relative humidities, 30, 40 and 50%. Experimental constants were determined for the selected thin layer drying models (Lewis, Page, Thompson, and moisture diffusion models), which are widely used for predicting the moisture contents of grains, and the suitability of these models was compared. The suitability of each of the four drying equations was verified using their predicted values for white beans as well as the determination coefficient ($R^2$) and the root mean square error (RMSE) of the experiment results. Results: It was found that the Thompson model was the most suitable for white beans with a $R^2$ of 0.97 or greater and RMSE of 0.0508 or less. The Thompson model was also found to be the most suitable for black beans, with a $R^2$ of 0.97 or greater and an RMSE of 0.0308 or less. Conclusions: The Thompson model was the most appropriate prediction drying model for white and black beans. Empirical constants for the Thompson model were developed in accordance with the conditions of drying temperature and relative humidity.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.896-905
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• 1993

Thin-layers of wheat and barley are dried at near-ambient temperatures(3.5$^{\circ}C$ -5$0^{\circ}C$) in order to obtain the intrinsic drying data. The well established apparatus was modified to enable it to record all the sample weight data in still air by using a purpose -built automatically controlled sliding valve. The air could be diverted in less than 0.5seconds and a 7 second period was required to attain a steady weight reading. With this apparatus, very smooth drying curves were obtained. The data of sample weight , drying temperature and dew point temperature wee recorded continuously . The drying process was terminated when the moisture content change in 24 hours was less than 0.004 d.b. This was achieved by drying a sample for about a week . The final points were recorded as the dynamic equilibrium moisture content(EMC). The drying data were than fitted to the exponential Newton model and the dynamic EMC data were fitted to the Modified-Chung-Pfost Model . All the fitted parameters are given and comparison is made with previous published data. The comparisons who that the current drying constants are lower than the previous data, the dynamic EMC data obtained for wheat and barely agree with the previous data. The results show that to obtain the drying constant in the exponential Newton model, adequate drying time is necessary.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.296-302
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• 2008

The effective moisture diffusivity and its dependence on drying temperature during drying of rapeseed were experimentally investigated. The data were recorded from thin layer drying experiments at nine different combinations of drying air temperatures of 40, 50, and $60^{\circ}C$ and the relative humidities of 30, 45, and 60%. The moisture diffusion equation was analyzed using stepwise multiple regression analysis. Effective moisture diffusivities were calculated based on the moisture diffusion equation for a spherical shape using Fick's second law. The effective diffusivities during the drying of rapeseed were $l.72{\times}10^{-11}$, $2.41{\times}10^{-11}$ and $3.31{\times}10^{-11}\;m^2{\cdot}s^{-1}$ at 40, 50 and $60^{\circ}C$, respectively. The activation energy for moisture diffusion during drying was $28.47\;kJ{\cdot}mol^{-1}$. The dependence of moisture diffusivity on temperature was described by an Arrhenius-type equation. Drying occurred in the falling rate period and the internal moisture diffusion phenomenon is the governing physical mechanism of the moisture movement in the particles.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.342-356
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• 2016

Purpose: The drying of a thin layer of native cassava starch in a tray dryer was modeled to establish an equation for predicting the drying behavior under given conditions. Methods: Drying tests were performed using samples of native cassava starch over a temperature range of $40-60^{\circ}C$. We investigated the variation in the drying time, dynamic equilibrium moisture content, drying rate period, critical moisture content, and effective diffusivity of the starch with temperature. The starch diffusion coefficient and drying activation energy were determined. A modification of the model developed by Hii et al. was devised and tested alongside fourteen other models. Results: For starch with an initial moisture content of 82% (db), the drying time and dynamic equilibrium moisture content decreased as the temperature increased. The constant drying rate phase preceded the falling rate phase between $40-55^{\circ}C$. Drying at $60^{\circ}C$ occurred only in the falling rate phase. The critical moisture content was observed in the $40-55^{\circ}C$ range and increased with the temperature. The effective diffusivity of the starch increased as the drying temperature increased from 40 to $60^{\circ}C$. The modified Hii et al. model produced randomized residual plots, the highest $R^2$, and the lowest standard error of estimates. Conclusions: Drying time decreased linearly with an increase in the temperature, while the decrease in the moisture content was linear between $40-55^{\circ}C$. The constant drying rate phase occurred without any period of induction over a temperature range of $40-55^{\circ}C$ prior to the falling rate period, while drying at $60^{\circ}C$ took place only in the falling rate phase. The effective diffusivity had an Arrhenius relationship with the temperature. The modified Hii et al. model proved to be optimum for predicting the drying behavior of the starch in the tray dryer.

• Journal of Biosystems Engineering
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• v.8 no.2
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• pp.62-68
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• 1983

This study was to examine the drying characteristics of barley and the effect of moisture content of barley on milling performance. A barley variety, Jogang, having 35.0% of initial moisture content was used for this experiment. Thermo-hydrostatic dryer which consists of blower, condensor, heater, humidifier, drying chamber and control box, etc., was used for the drying experiment. The change in the weight of a barley sample was continuously measured by means of the ring type load cell installed inside the drying chamber. Milling test runs the samples having the predetermined moisture content were taken from each drying test run. A laboratory type barley miller was used for the milling test. The results of the study are summarized as follows: 1. The drying constants (k) applied for the thin layer drying model, (M-Me)/(Mo-Me) = $Ae^{-kt}$ were 0.155, 0.259 and 0.548, respectively, at the three levels of drying temperatures, $40^{\circ}C$, $50^{\circ}C$ and $60^{\circ}C$. The drying constants complied with the Arrhenius Equation, K = Ko exp (-C/T), were determined as $Ko=1.901455{\times}10^8$ and C = 6563. 2. The laboratory milling test indicated that the highest milled and head barley recovery was resulted from the sample which was dryed at $40^{\circ}C$. In general, the increase in the drying temperature from $40^{\circ}C$ to $60^{\circ}C$ indicated a negative effect on milling yields. 3. Also, the sample having 15% M.C. presented the highest milled and head barley recovery among the five moisture content levels (12, 15, 18,21 and 24%).