- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 386214, 18 pages
Thin-Layer Drying Characteristics and Modeling of Chinese Jujubes
1College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
2College of Mechanical and Electronic Engineering, Tarim University, Alar 843300, China
3Institute of Grain Machinery Research, Jilin Academy of Agricultural Machinery, Changchun 130022, China
Received 8 December 2011; Accepted 31 January 2012
Academic Editor: Zhijun Zhang
Copyright © 2012 Xiao-Kang Yi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A mathematical modeling of thin-layer drying of jujubes in a convective dryer was established under controlled conditions of temperature and velocity. The drying process took place both in the accelerating rate and falling rate period. We observed that higher temperature reduced the drying time, indicating higher drying rates of jujubes. The experimental drying data of jujubes were used to fit ten different thin-layer models, then drying rate constants and coefficients of models tested were determined by nonlinear regression analysis using the Statistical Computer Program. As for all the drying models, the Weibull distribution model was superior and best predicted the experimental values. Therefore, this model can be used to facilitate dryer design and promote efficient dryer operation by simulation and optimization of the drying processes. The volumetric shrinkable coefficient of jujubes decreased as the drying air temperature increased.
Jujube (Zizyphus jujuba Mill.) is a characterized Chinese fruit, whose cultivation area has reached more than 1.5 million hectares in China. Moreover, a lot of products are processed from jujubes, such as candied and drunk jujubes, and jujube tea, juice, and sugar. The vast majority of jujubes are dried and sold at home and abroad, except that a small proportion are reserved for fresh eating . Jujubes play an important role in human nutrition as sources of sugar, vitamins, protein, and minerals . For thousands of years, besides been used as food, jujube has been commonly used in traditional Chinese medicine. Jujubes tend to spoil because of their high moisture contents, which result in the production losses of 25–30% after harvest . Drying is one of the widely used methods for postharvest preservation of agricultural products. It is used to decrease considerable moisture content, reduce microbiological activity, and enable storability of the product under ambient temperatures [4, 5]. Dried food has longer shelf life in packages and lower transportation, handling, and storage costs .
Drying is a complicated process relating to simultaneous heat and mass transfer where water is transferred by diffusion from inside the food material to the air-food interface and from the interface to the air stream by convection [7, 8]. The amount of energy required to dry a product depends on many factors, such as initial moisture, desired final moisture, and drying air temperature and velocity . Thin layer drying means to dry as one layer of sample particles or slices. Thin layer drying equations have been used for drying time prediction for generalization of drying curves . Various mathematical models describing the drying characteristics of different fruits and vegetables have been proposed to optimize the drying process and design efficient dryers [11, 12]. There are many studies on the drying of fruits and vegetables, such as apricot , banana , carrot , fig , golden apples, grape , green pepper, stuffed pepper, green bean , litchi , mushroom, pistachio , onion , and pumpkin . Several researchers have investigated the drying kinetics of various agricultural products in order to evaluate the Weibull distribution model for describing the thin-layer drying characteristics [23, 24]. In addition, some researchers proposed the drying properties and processing technologies of jujubes [25–28]. The Henderson and Pabis model (see Table 2) has been applied in the drying of jujubes ; however, although this model may describe the drying curve for the specific experiments conditions, it cannot give a clear and accurate view of the important processes during drying.
The objectives of this study are (1) to determine the effect of air temperature and air velocity on the drying of jujube and to obtain drying curves; (2) to establish a mathematical model for predicting the thin layer drying characteristics of convection drying of jujubes at different drying air temperature and velocity conditions.
2. Materials and Methods
Bioer jujube is one of the main Chinese jujubes varieties, which mainly grow in Shanxi and Xinjiang Province. Bioer jujubes used in the experiments were produced in Alar city, Xinjiang province and were chosen as drying materials in September, 2010. The appearances of jujubes were presented in Figure 1. The samples in the same species with full maturity and uniform size were stored in a refrigerator at 4°C before starting the experiments. The initial moisture content was about 70.12% wet basis (w.b.).
2.2. The Laboratory Dryer
The drying experiments were carried out by a laboratory dryer (BG-II) manufactured at College of Biological and Agricultural Engineering, Jilin University, Changchun city, Jilin province. A schematic view of the experimental arrangement was shown in Figure 2.
The overall dimensions of the dryer are 2.2 × 0.6 × 1.8 m and it mainly consisted of a fan, electrical heater, drying chamber, and temperature and humidity control unit. The favourable drying air velocity provided by the fan could be changed by the electrical motor without level. A 0–15 m/s range anemometer (LUTRON, AM-4201, Taiwan) measured the velocity of air passing through the system. The drying air temperature was automatically controlled by regulating the required voltage to the heaters inside the air channel. The heater consisted of four groups of resistance wires of 1,000 W, and each group could be used independently to control the temperature (30–110°C, dry bulb temperature) of air and in drying chamber. The dry bulb temperature inside the drying chamber was measured and controlled with an accuracy of ±0.1°C using a Pt 100, 1/10 DIN, thermometer inserted in the middle position of the inlet cross-section. Temperature-humidity sensor (GALLTEC, TFK80J, Germany) was used to measure the relative humidity with an accuracy of ±3%Rh. Resistance wires were on and off by the control unit based on temperature change to maintain adjusted temperature at the same level during the experiments. A digital electronic balance (OHAUS, CP3102, USA) in the measurement range of 0–3100 g and an accuracy of 0.01 g was used for the moisture loss of samples.
2.3. Drying Procedures
Drying experiments were carried out at different drying temperatures of 45, 55, and 65°C and different velocities of 0.5, 1.0, and 2.0 m/s. The drying air temperature was automatically controlled at ±1°C by regulating an electrical heating device and the air velocity was measured by an anemometer at precision of 0.1%.
The dryer took some time to reach the desired value after starting up. Approximately 200 g of samples were put into a stainless-steel mesh bucket of 200 mm diameter, and then they were put into the dryer after weighting. In all the experiments, samples were kept the same thickness and tiled into the layers. The weighing interval of the drying samples was 1 h during the drying process. Since the weighing process only took a few seconds, no considerable disturbances were imposed. According to the standards set by General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) , the drying process was continued until the moisture content of the samples reached below 25% (w.b.). After the drying experiments, the samples were put into an electric constant temperature blower oven, maintaining at 105 ± 2°C until their weight remained unchanged. This weight was used to calculate the moisture content of the samples.
2.4. Experimental Uncertainly
Errors and uncertainties in experiments can arise from instrument selection, condition, calibration, environment, observation, reading, and test planning . In the drying experiments of jujubes, the temperatures, velocity of drying air, and weight losses were measured with appropriate instruments. During the measurements of the parameters, the uncertainties occurred were presented in Table 1.
2.5. Theoretical Considerations and Mathematical Formulation
Moisture contents of jujubes during thin layer drying experiments were expressed in dimensionless form as moisture ratios MR with the following equation [32, 33]: is the mean jujubes moisture content, is the initial value, and is the equilibrium moisture content. The drying rates of jujubes were calculated by using (2.2) [34, 35] and are the moisture content at and moisture content at (kg water/kg dry matter), respectively, is the drying time ().
Convection drying of fruits occurs in the falling rate drying period, thus the well-known semiempirical and empirical models could be applied to the drying data. To select a suitable model for describing the drying process of jujubes, drying curves were fitted with 10 thin-layer drying moisture ratio models (Table 2). During the analysis of mathematical drying models, it was assumed that materials contained the same initial moisture content; there was no heat loss with insulation of dryer walls; material internal temperature gradient, drying air humidity, and heat transfer between materials and volume contraction rate during drying were negligible.
The regression analysis was performed with the STATISTICA computer program developed by Statistical Package for Social Science (SPSS) 18. The coefficient of determination was one of the primary criteria when selecting the best equation to account for variation in the drying curves of dried samples [36–38]. In addition, the goodness of fit was determined by various statistical parameters such as reduced chi-square, mean bias error and root mean square error (RMSE). For a qualified fit, should be high while and RMSE are low [39, 40]. These statistical values are calculated as follows: is the th experimental moisture ratio, is the th predicted moisture ratio, is the number of observations, and is the number of constants in the drying model.
3. Results and Discussion
3.1. Drying Characteristics of Jujubes
The changes in moisture ratios with time for different drying air temperatures are shown in Figure 3. The final moisture content of samples dried under different conditions ranged from 28% to 25% (w.b.). The drying rate is higher for higher air temperature. As a result, the time taken to reach the final moisture content is less, as shown in Figure 3. Therefore, the drying air temperature has an important effect on the drying of jujubes. The changes of the moisture ratios at different air velocities (0.5, 1.0 and 2.0 m/s) under each air temperature (45, 55, and 65°C) were shown in Figure 4. The data revealed that the drying air velocities had little effect on the drying process. Similar results have been reported for plum , rosehip .
3.2. Drying Rate of Jujubes
The changes in the drying rates with moisture content for different drying air temperatures and velocities are shown in Figures 5 and 6. It is apparent that the drying process involved two periods, accelerating period and falling period, without a constant-rate drying period. At the beginning of the drying process, the drying rate increases rapidly with decreasing moisture content and reaches the maximum. Then drying rate decreases continuously with decreasing moisture content, and the drying operations are seen to occur in the falling rate period. It was also noted the predominant direct effect of air temperature on the drying rate, as clearly shown in the figures for all temperatures. Due to the fact that the relative humidity of the drying air at a higher temperature was less compared to that at a lower temperature, the difference in the partial vapor pressure between the radishes and their surroundings was greater for the higher temperature drying environment . The data revealed that the drying air velocities had little effect on the drying rate. These results are in agreement with the previous works [31, 44].
3.3. Mathematical Modelling of Thin-Layer Drying
The mathematical drying models were based on the experimental moisture contents and dry weights. Then continuous data were obtained at different drying air temperatures and velocities and they were converted into moisture ratios and fitted over drying time. According to the statistical results of the determination coefficient , chi-square (), and RMSE, ten thin-layer drying models were compared and shown in Table 3. The data showed that the highest coefficient (), and the lowest chi-square () and RMSE were obtained with the Weibull distribution model. Consequently, it could be concluded that the Weibull distribution model could sufficiently define the thin layer drying of jujubes. The model could be expressed in the following equation: is the moisture ratio; is drying rate constant (); is time (); , and is experimental constants. , changed between 0.997498 and 0.999960; , 0.000003–0.000144; RMSE, 0.001523–0.010796.
The Weibull distribution model was analyzed according to the different drying air temperatures and velocity conditions. The individual constants were obtained (Table 4). Furthermore, the multiple regression analysis was adopted to determine the relationship between drying air temperature, velocity, and the drying constants , , , based on the drying experiment data. All possible combinations of different drying variables were tested and included in the regression analysis . The drying constants and coefficients of the model were as follows: These expressions could be used to accurately predict the moisture ratio at any time during a drying process. The consistency of the Weibull distribution model and relationships between the coefficients and drying variables were shown in Table 5. As shown, changed between 0.993345 and 0.999878, was between 0.000008 and 0.000133, and RMSE was between 0.002704 and 0.018925.
In Figure 7, we compared the experimental and predicted moisture ratio at different air temperatures (45, 55 and 65°C) under each velocity (0.5, 1.0 and 2.0 m/s). It could be concluded that the established model was in good agreement with the experimental results at all drying conditions. In this picture figure, a higher drying air temperature produced a higher drying rate and the moisture ratio decreased faster.
To verify the established mathematical drying model, the experimental and predicted values of the moisture ratio at some particular drying conditions were compared. These values were located near a straight line of 45°, as shown in Figure 8, indicating that the drying data were well fitted with the model. Thus, the drying model could be used to well describe the thin-layer drying characteristics of jujubes.
3.4. Volume Shrinkage
Jujubes have a porous structure. There are volume shrinkage and deformation during drying process. If ignoring the thermal expansion of materials, the volume shrinkage coefficient equation is expressed as  where is the volume of jujubes and is the wet-based average moisture content. Assuming that is constant during the drying process, the above equation can be transformed as follow: The coefficients of volume shrinkage at different drying air temperatures are shown in Figure 9. We can see from the figure that the coefficients, which rang from 0.011 to 0.020, decline with the increase of air temperature under same air velocity, due to the larger changes of moisture content at higher temperature. The coefficients, measured by least-square method, are shown in Table 6.
Figure 10 shows that the volume changes with time at different drying air temperatures. The figure shows that the changing trend is basically identical with the change of moisture content. It means that with the increase of air temperature, the material shrinkage becomes more and more obvious during the drying process. Thus, when we choose air temperature, not only the drying rate but also the morphology and quality of the products should be taken into consideration.
Thin-layer drying of jujubes were investigated in this study. Ten models selected from the literatures were referred to illustrate the characteristics of the drying process and establish mathematical drying models of jujubes. Drying process for jujubes involved two periods, accelerating rate and falling rate period, no constant-rate period of drying was observed. After comparing the calculated , , and RMSE in each model, Weibull distribution model showed the best agreement with the experimental data. Furthermore, the effects of the drying air temperatures and velocities on the drying constants and coefficients of the Weibull distribution model were closely examined. We found that this model could be used to predict the moisture ratios of the jujubes during a drying process at any time, particularly at drying temperatures of 45–65°C and velocities of 0.5–2.0 m/s. Moreover, our results showed that the drying air temperature had a bigger effect on drying rate than the velocity. The volumetric shrinkable coefficient of jujubes was found to be in the range of 0.011–0.020 at drying temperatures of 45–65°C.
|, , , , , :||Drying coefficients|
|, :||Drying constants|
|:||Moisture content at any time, kg water/kg dry matter|
|:||Equilibrium moisture content, kg water/kg dry matter|
|:||Initial moisture content, kg water/kg dry matter|
|:||Moisture content at , kg water/kg dry matter|
|:||Moisture content at , kg water/kg dry matter|
|:||Dimensionless moisture ratio|
|:||Experimental dimensionless moisture ratio|
|MRpre:||Predicted dimensionless moisture ratio|
|:||Number of observations|
|:||Coefficient of determination|
|RMSE:||Root mean square error|
|:||Number of drying constants|
|:||Initial volume, mL|
|:||Volumetric shrinkable coefficient.|
This work is supported by National Natural Science Foundation of China (no. 10964009).
- Y. F. Cao, S. J. Li, F. M. Zhao, and D. A. Su, “Introduction on the development, problems and solutions in the jujube industry of China,” Packaging and Food Machinery, vol. 27, no. 4, pp. 46–49, 2009.
- C. G. Lei, J. P. Chen, and D. X. Lu, “The nutritive value and health function of zipiphi jujubes,” Progress in Modern Biomedicine, vol. 6, no. 3, pp. 56–57, 2006.
- Y. X. Zhang and D. H. Liu, “The processing status and developmental foreground of Chinese date in our country,” Storage & Process, vol. 5, pp. 15–17, 2008.
- R. K. Goyal, A. R. P. Kingsly, M. R. Manikantan, and S. M. Ilyas, “Mathematical modelling of thin layer drying kinetics of plum in a tunnel dryer,” Journal of Food Engineering, vol. 79, no. 1, pp. 176–180, 2007.
- A. Maskan, S. Kaya, and M. Maskan, “Hot air and sun drying of grape leather (pestil),” Journal of Food Engineering, vol. 54, no. 1, pp. 81–88, 2002.
- I. Doymaz, “Drying kinetics of white mulberry,” Journal of Food Engineering, vol. 61, no. 3, pp. 341–346, 2004.
- H. O. Menges and C. Ertekin, “Mathematical modeling of thin layer drying of Golden apples,” Journal of Food Engineering, vol. 77, no. 1, pp. 119–125, 2006.
- V. R. Sagar and P. Suresh Kumar, “Recent advances in drying and dehydration of fruits and vegetables: a review,” Journal of Food Science and Technology, vol. 47, no. 1, pp. 15–26, 2010.
- M. A. Karim and M. N. A. Hawlader, “Drying characteristics of banana: theoretical modelling and experimental validation,” Journal of Food Engineering, vol. 70, no. 1, pp. 35–45, 2005.
- V. T. Karathanos and V. G. Belessiotis, “Application of a thin-layer equation to drying data of fresh and semi-dried fruits,” Journal of Agricultural Engineering Research, vol. 74, no. 4, pp. 355–361, 1999.
- J. A. Hernandez, G. Pavon, and M. A. Garcia, “Analytical solution of mass transfer equation considering shrinkage for modeling food drying kinetics,” Journal of Food Engineering, vol. 45, pp. 1–10, 2000.
- H. O. Menges and C. Ertekin, “Thin layer drying model for treated and untreated Stanley plums,” Energy Conversion and Management, vol. 47, no. 15-16, pp. 2337–2348, 2006.
- J. Bon, C. Rosselló, A. Femenia, V. Eim, and S. Simal, “Mathematical modeling of drying kinetics for apricots: influence of the external resistance to mass transfer,” Drying Technology, vol. 25, no. 11, pp. 1829–1835, 2007.
- R. Dandamrongrak, G. Young, and R. Mason, “Evaluation of various pre-treatments for the dehydration of banana and selection of suitable drying models,” Journal of Food Engineering, vol. 55, no. 2, pp. 139–146, 2002.
- T. K. Gachovska, A. A. Adedeji, M. Ngadi, and G. V. S. Raghavan, “Drying characteristics of pulsed electric field-treated carrot,” Drying Technology, vol. 26, no. 10, pp. 1244–1250, 2008.
- S. J. Babalis, E. Papanicolaou, N. Kyriakis, and V. G. Belessiotis, “Evaluation of thin-layer drying models for describing drying kinetics of figs (Ficus carica),” Journal of Food Engineering, vol. 75, no. 2, pp. 205–214, 2006.
- O. Yaldiz, C. Ertekin, and H. I. Uzun, “Mathematical modeling of thin layer solar drying of sultana grapes,” Energy, vol. 26, no. 5, pp. 457–465, 2001.
- O. Yaldiz, C. Ertekin, and H. I. Uzun, “Mathematical modeling of thin layer solar drying of sultana grapes,” Energy, vol. 26, no. 5, pp. 457–465, 2001.
- S. Janjai, M. Precoppe, N. Lamlert et al., “Thin-layer drying of litchi (Litchi chinensis Sonn.),” Food and Bioproducts Processing, 2010.
- A. Midilli, H. Kucuk, and Z. Yapar, “A new model for single-layer drying,” Drying Technology, vol. 20, no. 7, pp. 1503–1513, 2002.
- R. L. Sawhney, P. N. Sarsavadia, D. R. Pangavhane, and S. P. Singh, “Determination of drying constants and their dependence on drying air parameters for thin layer onion drying,” Drying Technology, vol. 17, no. 1-2, pp. 299–315, 1999.
- K. Sacilik, “Effect of drying methods on thin-layer drying characteristics of hull-less seed pumpkin (Cucurbita pepo L.),” Journal of Food Engineering, vol. 79, no. 1, pp. 23–30, 2007.
- O. Corzo, N. Bracho, A. Pereira, and A. Vásquez, “Weibull distribution for modeling air drying of coroba slices,” Food Science and Technology, vol. 41, no. 10, pp. 2023–2028, 2008.
- E. Uribe, A. Vega-Gálvez, K. Di Scala, R. Oyanadel, J. Saavedra Torrico, and M. Miranda, “Characteristics of convective drying of pepino fruit (Solanum muricatum Ait.): application of weibull distribution,” Food and Bioprocess Technology, pp. 4–8, 2009.
- J. P. Chen, Q. Y. Mu, and C. R. Tian, “Study on the effect of the different heating processes on the quality of the Chinese date,” Transactions of the Chinese Society of Agricultural Engineering, vol. 15, no. 3, pp. 237–240, 1999.
- M. H. Cui, “Solar convective drying properties of candied date,” Journal of Hebei University of Science and Technology, vol. 20, no. 2, pp. 72–76, 1999.
- M. H. Cui, “Study on the characteristics of candied dates' convectional drying mass transfer,” Journal of Hebei Normal University, vol. 30, no. 4, pp. 450–452, 2006.
- X. Yang, Y. Xie, and G. Jin, “Improved scheme and test comparison of drying jujube date using heat pump,” Transactions of the Chinese Society of Agricultural Engineering, vol. 25, no. 9, pp. 329–332, 2009.
- Z. Y. Niu, H. Q. Tan, and L. Zong, “Experimental research on the drying characteristics of jujubes,” Food & Machinery, vol. 5, pp. 18–20, 1998.
- General Administration of Quality Supervision, Inspection and Quarantine, Dried Chinese Jujubes, Administration of Quality Supervision, Inspection and Quarantine (AQSIQ), Beijing, China, 2009.
- E. Akpinar, A. Midilli, and Y. Bicer, “Single layer drying behaviour of potato slices in a convective cyclone dryer and mathematical modeling,” Energy Conversion and Management, vol. 44, no. 10, pp. 1689–1705, 2003.
- A. Midilli, H. Olgun, and T. Ayhan, “Experimental studies on mushroom and pollen drying,” International Journal of Energy Research, vol. 23, no. 13, pp. 1143–1152, 1999.
- A. Midilli, “Determination of pistachio drying behaviour and conditions in a solar drying system,” International Journal of Energy Research, vol. 25, no. 8, pp. 715–725, 2001.
- L. M. Diamente and P. A. Munro, “Mathematical modelling of hot air drying of sweet potato slices,” International Journal of Food Science and Technology, vol. 26, no. 1, pp. 99–109, 1991.
- A. Midilli and H. Kucuk, “Mathematical modeling of thin layer drying of pistachio by using solar energy,” Energy Conversion and Management, vol. 44, no. 7, pp. 1111–1122, 2003.
- C. Ertekin and O. Yaldiz, “Drying of eggplant and selection of a suitable thin layer drying model,” Journal of Food Engineering, vol. 63, no. 3, pp. 349–359, 2004.
- P. S. Madamba, R. H. Driscoll, and K. A. Buckle, “The thin-layer drying characteristics of garlic slices,” Journal of Food Engineering, vol. 29, no. 1, pp. 75–97, 1996.
- M. Ozdemir and Y. Onur Devres, “Thin layer drying characteristics of hazelnuts during roasting,” Journal of Food Engineering, vol. 42, no. 4, pp. 225–233, 1999.
- I. T. Togrul and D. Pehlivan, “Mathematical modelling of solar drying of apricots in thin layers,” Journal of Food Engineering, vol. 55, no. 3, pp. 209–216, 2002.
- D. R. Pangavhane, R. L. Sawhney, and P. N. Sarsavadia, “Effect of various dipping pretreatment on drying kinetics of Thompson seedless grapes,” Journal of Food Engineering, vol. 39, no. 2, pp. 211–216, 1999.
- I. Doymaz, “Effect of dipping treatment on air drying of plums,” Journal of Food Engineering, vol. 64, no. 4, pp. 465–470, 2004.
- S. Erenturk, M. S. Gulaboglu, and S. Gultekin, “The thin-layer drying characteristics of rosehip,” Biosystems Engineering, vol. 89, no. 2, pp. 159–166, 2004.
- S. Kaleemullah and R. Kailappan, “Monolayer moisture, free energy change and fractionation of bound water of red chillies,” Journal of Stored Products Research, vol. 43, no. 2, pp. 104–110, 2007.
- S. J. Babalis and V. G. Belessiotis, “Influence of the drying conditions on the drying constants and moisture diffusivity during the thin-layer drying of figs,” Food Science and Technology, vol. 42, pp. 180–186, 2009.
- E. K. Akpinar, “Determination of suitable thin layer drying curve model for some vegetables and fruits,” Journal of Food Engineering, vol. 73, no. 1, pp. 75–84, 2006.
- S. E. Charm, The Fundamentals of Food Engineering, AVI Publishing Company Inc., Westport, Conn, USA, 3rd edition, 1978.
- W. K. Lewis, “The rate of drying of solid materials,” Industrial Engineering Chemistry, vol. 13, no. 5, pp. 427–432, 1921.
- G. E. Page, Factors influencing the maximum rates of air drying shelled corn in thin layers, M.S. thesis, Purdue University, West Lafayette, Ind, U.S., 1949.
- C. Y. Wang and R. P. Singh, “Use of variable equilibrium moisture content in modelling rice drying,” ASAE Paper 78-6505, American Society of Agricultural and Biological Engineers, St. Joseph, Mich, USA, 1978.
- D. G. Overhults, G. M. White, H. E. Hamilton, and I. J. Ross, “Drying soybean with heated air,” Transactions of the American Society of Agricultural Engineers, vol. 16, no. 1, pp. 112–113, 1973.
- S. M. Henderson and S. Pabis, “Grain drying theory. 1. Temperature effect on drying coefficient,” Journal of Agricultural Engineering Research, vol. 6, pp. 169–174, 1961.
- Y. I. Sharaf-Eldeen, J. L. Blaisdell, and M. Y. Hamdy, “A model for ear corn drying,” Transactions of the American Society of Agricultural Engineers, vol. 23, no. 5, pp. 1261–1271, 1980.
- T. L. Thompson, R. M. Peart, and G. H. Foster, “Mathematical simulation of corn drying: a new model,” Transactions of the American Society of Agricultural Engineers, vol. 11, pp. 582–586, 1968.
- W. Weibull, “A statistical distribution of wide applicability,” Journal of Applied Mechanics, vol. 18, pp. 293–297, 1951.