OJAppS  Vol.11 No.3 , March 2021
Performance Analysis and Drying Kinetics of Maize in an AflaSTOP Dryer
Abstract: The performance of an AflaSTOP dryer which utilises biomass energy for drying maize was investigated. The drying behaviour of maize grains in the dryer was also investigated using ten (10) thin-layer mathematical models. The models were compared based on coefficient of determination (R2) and Root Mean Square Error (RMSE) values between experimental and predicted moisture ratios. At an average drying air temperature of 50°C and drying air velocity of 2.5 m/s, maize at average moisture content (MC) of 17.5% (wb) was dried to an average MC of 11.5% (wb) in three (3) hours. The drying and thermal efficiency were calculated as 81.1% and 29.6% respectively. Overall, drying took place in the falling rate period. The Logistics model was best to describe the thin-layer drying kinetics of maize in the dryer with R2 value of 0.9902 and RMSE value of 0.04908.

1. Introduction

Maize (Zeamays L.) is an economic staple food in the world especially Sub-Sahara Africa. In Sub-Saharan Africa, it is considered as the most important cereal crop which is cultivated across a large range of ecological zones. According to [1], maize makes a significant contribution towards food security in Sub-Saharan Africa by contributing over 30% of the daily dietary energy consumption. Drying maize after harvest from high moisture content of 20% - 30% to low safe storage moisture content (<13%) is necessary to ensure storability of maize grains in warm and humid countries like Ghana. However, drying of moist materials is a complicated process involving simultaneous heat and mass transfer [2]. During maize drying, it is recommended that the drying air temperature does not exceed 55˚C for seed maize (restricted to avoid killing the germ) and 90˚C for consumption (restricted to avoid cooking the maize kernel and causing fissures of the outer most layer) [3].

In Ghana, smallholder farmers still depend on open-air/sun drying either on the bare ground by the road side or at the market place on tarpaulins. According to [4], this method is associated with various limitations including; dependence on weather conditions, labour intensive, unhygienic, unreliable, time-consuming, non-uniform drying and requiring most often large area for spreading the product out to dry and in some places, have failed to produce expected quality. Losses up to 30% according to [5] also occur in the daily labor-intensive handling process of spreading crops on the ground, and then gathering and re-bagging them.

Due to the several limitations of the open-air/sun drying method, other efficient and suitable drying methods have been introduced. Most of such dryers utilise electricity, fossil fuels and gas as their source of energy. However, these drying systems usually built for large scale drying have high energy demand and are also very expensive to secure and operate [6]. In addition, farmers are often not prepared to pay for the high drying charges of these drying systems, which are also often centralized and far from farmers, thereby discouraging their patronage.

In Ghana, majority of smallholder maize farmers farm on average land size of 2 hectares with an average production yield of 1.8 MT/ha. With virtually no available alternative drying technologies tailored for the smallholder farmer, access to low cost batch drying systems that can be owned and operated at their level are desirable. The development of the AflaSTOP dryer, a portable batch dryer that can be incorporated into a farmer’s normal post-harvest routine is widely considered a viable drying option that allows smallholder farmers to dry their grains to safe storage moisture level at their convenience.

For commercialization and potential scale-up of the AflaSTOP dryer, numerous drying trials are required to rigorously ascertain its technical performance to reflect on its potential for full market diffusion. This can be achieved in addition to field trials using mathematical simulations to optimize the performance. According to [7], to successfully transfer knowledge acquired experimentally from studies on food dehydration into industrial applications, mathematical modelling of drying kinetics is required. Moreover [7] further stated that a mathematical model is an important tool used to optimize operating parameters and to predict performance of a drying system. Numerous mathematical models, empirical and semi-empirical, have been proposed to estimate the drying characteristics of agricultural products [8]. These simple models, also known as thin layer models, allow prediction of mass transfer during dehydration and are applied to simulate drying curves under similar conditions [9] [10]. According to [11], many thin-layer drying mathematical models have been developed for predicting drying behaviour of food and agricultural materials. Some of these include; Newton, Henderson and Pabis, Page, Simplified Ficks diffusion equation, Logistics, Two-term exponential models etc. These models are also relevant in the modification and optimization of drying systems for commercialisation as reported by [12] [13]. Studies into thin layer drying kinetics of maize in experimental drying systems are critical to support the potential scale-up of such drying technologies.

Although many studies have been conducted about drying properties for different agricultural products using different drying technologies, no study has been carried out on the drying kinetics of white maize using the AflaSTOP dryer. Additionally, drying indices of maize under drying conditions in the dryer is not available for performance optimization. Therefore, the aim of this study was to analyse the performance and determine a suitable mathematical model for predicting the drying kinetics of maize in a locally fabricated AflaSTOP dryer using thin layer drying models.

2. Materials and Methods

2.1. Description and Mode of Operation of the Dryer

As presented in Figure 1, the drying system consists of two units; 1) the shallow bed unit and 2) the heat (drying air) supply unit. Parts of the shallow bed unit include: collapsible support frame, center post, collapsible panel, side guard, roof, and tarpaulin base plenum. The heat (drying air) supply unit consists of parts including; furnace, two fans, chimney, attachment pin and a lock, pulley system, duct connector and plate heat exchangers. The dryer is developed to afford smallholder farmers the opportunity to dry their grains irrespective of location and can dry 3 batches per day. The dryer is designed to utilize agro residues such as maize cobs as its main heat source and can dry maize in batches of 500 kg,

Figure 1. Description of dryer [14].

lowering the moisture level from 18% - 20% to 12% - 14%. As shown in Figure 2, the biomass (maize cobs used in this study) is combusted in the combustion chamber (furnace). Heat generated is absorbed by the plate type heat exchangers which in turn heats the high velocity air generated by the intake fan with the aid of the engine connected by a v-belt. The heated air is blown into the plenum of the shallow bed unit through the connecting duct. Heated air in the plenum evaporates the moisture in the grains due to its low relative humidity.

The saturated air rises out into the atmosphere through the open vent in the shallow bed unit. The smoke generated in the combustion chamber is forced out by the exhaust fan through the chimney into the atmosphere. Maize is dried to the required moisture content if the process is continued for few hours with constant supply of heat depending on the initial moisture content of the grains.

2.2. Maize Variety

Obaatanpa”, a local white maize variety was obtained from the KNUST Agricultural Research Station farm for the drying experiment. The initial moisture content was determined using ISO 6540-1980 moisture content determination method for maize. 8 g of ground maize was dried in an oven at 130˚C for 4 h. Three replications were used for the determination of the initial moisture content and an initial average of 17.5% ± 0.86% wet basis was recorded.

2.3. Drying Procedure

Drying experiments were carried out using the AflaSTOP dryer (Figure 3) locally fabricated at the workshop of the Department of Agricultural and Biosystems Engineering, KNUST, Kumasi.

A total of 250 kg of “Obaatanpa” white shelled maize at initial MC of 17.5% (wb) was used for the drying experiment. The dryer powered by a 5-litre capacity diesel generator was allowed to run for at least 20 minutes before the commencement of drying for it to reach steady state condition. Drying air conditions

Figure 2. Schematic view of air movement during operation of AflaSTOP dryer [14].

Figure 3. Constructed AflaSTOP dryer at KNUST.

(temperature and relative humidity) were monitored at the plenum of the dryer using Kestrel data loggers positioned at 3 logging positions (Figure 4). Conditions of the ambient were also monitored during the drying process.

The MC of maize grains during the drying experiment was concurrently monitored hourly with a pre-calibrated moisture meter (Dicky-John mini GAC plus moisture analyser with accuracy of ±0.1%). Maize grains were sampled at different positions in the dryer and mixed thoroughly before the MC reading was taken and recorded. Three measurements were taken at each period and the means were calculated for analysis.

2.4. Theories and Calculations

• Thermal Efficiency

According to [15], the thermal efficiency of biomass burners with heat exchangers can be determined as stated in Equation (1).

( % ) η t h = Q s Q a × 100 (1)


Q s = M a i r × C a i r ( T a i r T a m b ) (2)

Q a = M c c H v (3)

M a i r = V a i r × D a i r (4)

Q a = Heat available for drying (kJ/kg);

Q s = Heat supplied (kJ/h);

T a i r = Average heated air temperature (˚C);

T a m b = Average ambient air temperature (˚C);

C p a i r = Specific heat capacity of air (kJ/kg∙˚C);

M a i r = Mass flow rate of air (kg/h);

M c c = Biomass (maize cobs) consumed (kJ/kg;)

Figure 4. Data logging and sampling positions during the drying experiment.

H v = Gross heating value of maize cob (kJ/kg);

V a i r = volume flow rate of air (m3/h);

D a i r = density of air (kg/m3).

• Drying System Efficiency

The drying system efficiency was determined from the formulae stated below. It is defined as the ratio of the heat used to evaporate moisture from the grains to the energy given to the drying air [16].

ŋ s y s = E u E f (5)

E u = M w × L v (6)

E f = M a × C p × ( T o u t T d ) (7)


E f = Energy given to the drying air;

E u = Energy used to evaporate mass of water;

M a = mass of air (kg);

C p = specific heat capacity of air (kJ/kg∙˚C);

l v = latent heat of vaporization (kJ/kg);

m w = mass of water (kg);

T o u t = temperature of air going out after drying;

T d = drying air temperature.

• Volume Flow Rate of Air (m3/h)

V = V a t (8)

where V, Va and t are the Volume flow rate, quantity of air (m3), and the total drying time respectively.

• Mass Flow rate of Air (kg/s)

m a i r = ρ V (9)

where mair, ρ and V are the mass flow rate of air (kg/s), density of air (kg/m3) and V volume flow rate of air (m3/h).

• Moisture Content

M C w b = M m M d M m (10)

Moisture loss (%)

= Initial moisture content % ( M i w b ) – Final moisture content % ( M f w b ) (11)

where M C w b , M m and M d are moisture content (wet basis), initial and finial mass of grains.

• Moisture extraction rate (MER)

M E R = M w t (12)

M w = M m M d (13)

where MER, Mw, t, Mm, and Md are the moisture extraction rate, mass of moisture loss, drying time, initial and finial mass of grains.

• Rate of moisture loss (Mr)

M r = Δ M C t (14)

where, Mr, ΔMC and t represents rate of moisture loss, change in moisture content and time taken for the drying process respectively.

• Volume of fuel consumed (Vf)

(Vf) = Level of fuel in tank after drying period

× Length of fuel tank (22 cm) × breath of fuel tank (20 cm) (15)


level of fuel consumed for drying = Initial level of fuel – finial level of fuel (16)

where Vf = volume of fuel used.

• Thin layer mathematical modelling

Key parameters monitored were MC of maize (maize drying rate), temperature and relative humidity in the dryer and the ambient formed the basis for the thin layer drying kinetics modelling in the dryer using experimental models.

• Determination of moisture ratio (MR)

The moisture ratio during the thin-layer drying of maize in the dryer was calculated using Equation (17).

M R = M t M e M o M e (17)

where MR is the dimensionless moisture ratio, Mt is the moisture content (% db) at time t, and Mo and Me are the initial and equilibrium moisture contents respectively, on dry weight basis. The values of Me are relatively small compared to Mt and Mo, where error implied in the simplification is negligible [17]. The MR was therefore simplified as shown:

M R = M t M o (18)

2.5. Mathematical Models

The moisture ratio (MR) calculated from the MC of maize grains during the drying experiment were fitted to 10 common thin-layer drying models (Table 1) widely used to describe the drying characteristics of most food products. The mathematical drying models were used to assess and predict the moisture ratio of maize at an average drying air temperature of 50˚C. The best model was selected to describe the drying processes of maize in the dryer. The best fit model to the experimental data was selected using statistical parameters such as the coefficient of determination (R2) and the root mean square error (RMSE) to assess the goodness of the fit. The best fit model was that which resulted in higher R2 and the lowest RMSE [18] [19].

2.6. Model Fitting

The fitness of experimental data to the thin-layer drying models was evaluated as reported by [11] where statistical parameters such as the root mean square error (RMSE) and coefficient of determination (R2) were calculated using Equations 19 and 20 respectively. For better fitting procedures, [29] reported that higher R2 and lower RMSE values should be obtained. Regression analysis was done using MATLAB (Version R2015a) statistical computer program.

R M S E = [ 1 N i = 1 N ( M R e x p , i M R p r e , i ) 2 ] 1 2 (19)

R 2 = 1 i = 1 N [ M R e x p , i M R p r e , i ] k = i N [ M R p r e , i k = 1 n M R p r e , i N ] (20)


M R p r e , i is the predicted moisture ratio; M R e x p , i is the experimental moisture ratio; N is the number of observations/readings.

Table 1. Thin layer drying models used for model analysis.

3. Results and Discussion

3.1. Variation in Temperature and Relative Humidity with Time

Figure 5 shows the temperature variation across the furnace and plenum compared to the ambient temperature. The plenum temperature varied between 38˚C at the start and peaking at 70˚C when the furnace combustion chamber was restocked with maize cobs. Overall an average plenum temperature of 52.8˚C was recorded while the average ambient temperature was 33.8˚C. The high temperature trend recorded in the plenum during the drying process was due to the high supply of heat energy from the combustion of maize cobs in the furnace of the AflaSTOP dryer. The recorded temperature in the dryer was high enough to dry the maize grains from the initial MC of 17.5% to 11.5% in 3 hours. According to [3], an average temperature of 50˚C is required for effective seed maize drying. The recorded temperature at the plenum of the dryer is suitable for drying seed maize as well.

Variation in the relative humidity (RH) of the ambient and plenum air is shown in Figure 6. The ambient had an average RH of 54.8% compared to the plenum average RH of 27.7%. The lower the RH of the air, the higher its ability to absorb moisture. The lower RH at the plenum of the dryer was due to the high temperature which created turbulent effect causing water molecules in the grains to evaporate. This phenomenon caused rapid moisture extraction in the grains as the water molecules were energized by the high drying air temperature to attain high kinetic energy to evaporate the moisture bound in the grains. The water molecules were easily absorbed carried away by the low RH of the surrounding air.

Figure 5. Temperature variation with time during experiment.

Figure 6. A graph of relative humidity variation with time.

3.2. Drying Curves

The moisture content (MC) of maize grains were monitored at hourly intervals by sampling grains at different positions in the shallow bed unit. The grains were mixed thoroughly before the measurement was done. The change in moisture content of maize with drying time in the AflaSTOP dryer is presented in Figure 7. It was observed that, moisture reduction in maize varied across the drying bed of the dryer with grains closer to the inlet of the heat source drying faster than grains further away from the heat source. However, on average, it was observed that, 3 hours of drying time was used to dry 250 kg of maize from a moisture content of 17.5% to an average moisture content of 11.5%. This resulted in a drying rate of about 2% moisture reduction per hour. In all, about 45 kg of maize cobs and 0.9 liters of diesel was used to evaporate 28.8 kg of water. Other performance indicators of the drying experiment is presented in Table 2.

The calculated thermal efficiency (29.6%) reveals the percentage of heat used to extract moisture from the grains. The drying system efficiency of 81.9% was high indicating the dryer is effective in removing moisture (29 kg) from the maize grains dried within the 3-hour period. Similar observations were made by [30] [31]. The study by [30] on the performance of a biomass fired dryer for copra drying reported a thermal and drying efficiency of 26% and 16% respectively. Work done by [31], on the performance of a biomass dryer for cashew nut drying also reported thermal efficiency and drying efficiency of 35% and 30.5% respectively.

Table 2. Experimental overview.

Figure 7. Moisture content of maize versus time.

3.3. Evaluation of Mathematical Models

Thin layer drying models presented in Table 1 were fitted to the experimental data. The values of coefficient of determination (R2) and RMSE for the selected models used to predict the drying behaviour of maize dried in the AflaSTOP dryer are presented in Table 3. The suitable model according to [11], is the model with the highest R2 value and the lowest RMSE value. The Logistics model satisfactorily described the drying kinetics of maize dried in the AflaSTOP dryer.

From Table 3, the Logistics model recorded the highest R2 value and the least RMSE values of 0.9902 and 0.04908 respectively. This makes it suitable for predicting the drying kinetics of the maize drying process in the AflaSTOP dryer. Drying of the maize grains occurred entirely in the falling rate period which involved moisture removal from the grains by the diffusion process where moisture from inside was transported towards the surface of the grains. During this period, the surface of a maize grain is not covered with a thin layer water, because the internal resistance to moisture transport has become greater than the external resistance. This mechanism of diffusion is best described by Fick’s second law of diffusion which assumes that the resistance to water diffusion in a product occurs in the outer layer of the product resulting in the continuous decrease in the drying rate during the course of drying [29]. Other studies have reported the selection of the Logistics model, a semi-theoretical drying model for predicting the drying behavior of some other crops during thin-lay drying experiments. The model has been successfully used to examine the drying kinetics of parsley [32], bamboo sheet [33], and rice [34].

3.4. Comparison of Experimental and Predicted Models for Moisture Ratios

The experimental and predicted data values using the Logistics model for moisture ratios of maize dried in the AflaSTOP dryer are shown in Figure 8. It shows the relationship between MRpre, MRexp and drying time. The overlapping data points show the closeness of the predicted and experimental values. It was observed that the Logistics model provided a very good conformity between the

Figure 8. Variation of experimental and predicted Moisture Ratio (MR) with time.

Table 3. Values of R2 and RMSE of 10 drying models.

experimental and the predicted moisture ratios and best describes the drying kinetics of maize using the AflaSTOP dryer.

Figure 9 presents the comparison of the MRexp and MRpre values of maize dried in the dryer. It was observed that, the predicted data banded around the ideal trend line indicating the suitability of the Logistic model in predicting the

Figure 9. Comparison of MRexp and MRpre for maize dried in the AflaSTOP dryer.

drying behaviour of maize in the AflaSTOP dryer. The values obtained from the model were almost the same showing a uniform declination in moisture content from the start of the drying process to the end. The declination in moisture ratio can be attributed to high drying temperature, low relative humidity and high air velocity of the drying air.

4. Conclusion

This study analysed the performance of the AflaSTOP dryer and successfully predicted the drying behaviour of maize in the dryer using thin layer models. At an average drying temperature of 50˚C and air velocity of 2.5 m/s, 250 kg of white maize at an initial moisture content of 17.5% (wb) was reduced to 11.5% at the conditions specified in the dryer for 3 hours. This resulted in a drying rate of 2%/h and moisture extraction rate of 9.6 kg/h. In all about 45 kg of maize cobs and 0.9 liters of diesel was used to evaporate 28.8 kg of water. This implies that for every litre of diesel, approximately 32.7 kg of moisture can be removed from maize using the AflaSTOP dryer. This gives a fuel utilized to moisture removed ratio of 1:33. Among ten (10) thin layer mathematical models analysed, the Logistics model was the best to describe the drying behaviour of maize in the dryer. It had the highest R2 value and lowest RMSE value of 0.9902 and 0.04908 respectively. The potential scale-up and adoption of the AflaSTOP dryer by smallholder maize farmers will contribute to reduce postharvest losses in maize at the smallholder level.

Cite this paper: Owusu-Sekyere, E. , Obeng-Akrofi, G. , Akowuah, J. and Maier, D. (2021) Performance Analysis and Drying Kinetics of Maize in an AflaSTOP Dryer. Open Journal of Applied Sciences, 11, 327-342. doi: 10.4236/ojapps.2021.113024.

[1]   İnternatİonal İnstİtute of Tropİcal Agrİculture (İİTA) (2014) Maİze.

[2]   Yİlbas, B.S., Hussaİn, M.M. and Dİncer, İ. (2003) Heat and Moİsture Dİffusİon İn Slab Products to Convectİve Boundary Condİtİon. Heat and Mass Transfer, 39, 471-476.

[3]   Rossouw, M. and Walker, S. (2015) User Experİence Evaluatİon and Qualİtatİve demand Analysİs of the Second-Generated (Beta) Prototype of the Potable Shallow Bed Batch Maİze Dryer (AflaSTOP).

[4]   Tonuİ, K.S., Mutaİ, E.B.K., Mutulİ, D.A., Mbuge, D.O. and Too, K.V. (2014) Desİgn and Evaluatİon of Solar Maİze Graİn Dryer wİth a Back-up Heater. Research Journal of Applİed Scİences, Engİneerİng and Technology, 15, 3036-3043.

[5]   Opİt, G.P., Campbell, J., Authur, F., Armstrong, P., Osekre, E., Washburn, S., Baban, O., McNeİll, S., Mbata, G., Ayobamİ, İ. and Reddy, P.V. (2014) A Report on Assessment of Maİze Postharvest Losses İn the Mİddle Belt of Ghana.İne-reports/Ghana%20-%20Fİrst%20PHL%20

[6]   Kaaya, A.N. and Kyamukangİre, W. (2010) Dryİng Maİze Usİng Bİomass-Heated Natural Convectİon Dryer İmproves Graİn Qualİty durİng Storage. Journal of Applİed Scİence, 10, 967-974.

[7]   Babalİs, S.J. and Belessİotİs, V.G. (2004) İnfluence of the Dryİng Condİtİons on the Dryİng Constants and Moİsture Dİffusİvİty durİng the Thİn-Layer Dryİng of Fİgs. Journal of Food Engİneerİng, 65, 449-458.

[8]   Mİrzaee, E., Rafİee, S. and Keyhanİ, A. (2010) Evaluatİon and Selectİon of Thİn-Layer Models for Dryİng Kİnetİcs of Aprİcot (cv. NASİRY). Agrİcultural Engİneerİng İnternatİonal: CİGR Journal, 12, 111-116.

[9]   Doymaz, İ. (2007) Aİr-Dryİng Characterİstİcs of Tomatoes. Journal of Food Engİneerİng, 78, 1291-1297.

[10]   Purkayastha, M.D., Nath, A., Deka, B.C. and Mahanta, C.L. (2013) Erratum to: Thİn Layer Dryİng of Tomato Slİces. Journal of Food Scİence and Technology, 50, 654.

[11]   Chayjan, R.A., Peyman, M.H., Esna-Asharİ, M. and Salarİ, K. (2011) İnfluence of Dryİng Condİtİons on Dİffusİvİty, Energy and Color of Seedless Grape after Dİppİng Process. Australİan Journal of Crop Scİence, 5, 96-103.

[12]   Corrêa, P.C., Botelho, F.M., Olİveİra, G.H.H., Gonelİ, A.L.D., Resende, O. and de Carvalho Campos, S. (2011) Mathematİcal Modelİng of the Dryİng Process of Corn Ears. Acta Scİentİarum Agronomy, 33, 575-581.

[13]   Gely, M.C. and Gİner, S.A. (2007) Dİffusİon Coeffİcİent Relatİonshİps durİng Dryİng of Soya Bean Cultİvars. Bİosystems Engİneerİng, 96, 213-222.

[14]   Unİted StatesAgency for İnternatİonal Development (USAİD) (2016) Assembly, Operatİon and Maİntenance of an Easy Dry M500.

[15]   Bİtog, J.P.P., Elaurİa, J.C., Elepaño, A.R. and Resurreccİon, A.N. (2009) Desİgn, Fabrİcatİon and Evaluatİon of a Dİrect-Fİred Corncob Furnace for Corn Dryİng. Phİlİppİnes Journal of Agrİcultural and Bİosystems Engİneerİng, 7, 3-15.

[16]   Balbİne, M., Marcel, E., Kuİtche, A. and Zeghamtİc, B. (2015) Experİmental Evaluatİon of the Thermal Performance of Dryer Aİrflow Confİguratİon. İnternatİonal Journal of Energy Engİneerİng, 5, 80-86.

[17]   Husseİn, J.B., Fİllİ, K.B. and Oke, M.O. (2016) Thİn Layer Modelİng of Hybrİd, Solar and Open Sun Dryİng of Tomato Slİces. Research Journal of Food Scİence and Nutrİtİon, 1, 15-27.

[18]   Duc, L.A., Han, J.W. and Keum. D.H. (2011) Thİn Layer Dryİng Characterİstİcs of Rapeseed (Brassİca napus L.). Journal of Stored Products Research, 47, 32-38.

[19]   Radhİka, G.B., Satyanarayana, S.V. and Rao, D.G. (2011) Mathematİcal Model on Thİn Layer Dryİng of Fİnger Mİllet (Eluesİne coracana). Advance Journal of Food Scİence and Technology, 3, 127-131.

[20]   Kaleta, A. and Górnİckİ, K. (2010) Some Remarks on Evaluatİon of Dryİng Models of Red Beet Partİcles. Energy Conversİon and Management, 51, 2967-2978.

[21]   Aktas, M., Ceylan, İ. and Yİlmaz, S. (2009) Determİnatİon of Dryİng Characterİstİcs of Apples İn a Heat Pump and Solar Dryer. Desalİnatİon, 239, 266-275.

[22]   Arumuganathan, T., Manİkantan, M.R., Raİ, R.D., Anandakumar, S. and Khare, V. (2009) Mathematİcal Modelİng of Dryİng Kİnetİcs of Mİlky Mushroom İn a Fluİdİzed Bed Dryer. İnternatİonal Agrophysİcs, 23, 1-7.

[23]   Arabhosseİnİ, A., Huİsman, W., Van Boxtel, A. and Muller, J. (2009) Modelİng of Thİn Layer Dryİng of Tarragon (Artemİsİa dracunculus L.). İndustrİal Crops Products, 29, 53-59.

[24]   Aghbashlo, M., Kİanmehr, M.H., Khanİ, S. and Ghasemİ, M. (2009) Mathematİcal Modellİng of Thİn-Layer Dryİng of Carrot. İnternatİonal Agrophysİcs, 23, 313-317.

[25]   TürkToğrul, İ. and Pehlİvan, D. (2002) Mathematİcal Modellİng of Solar Dryİng of Aprİcots İn Thİn Layers. Journal of Food Engİneerİng, 55, 209-216.

[26]   Mİdİllİ, A., Kucuk, H. and Yapara, Z. (2002) New Model for Sİngle-Layer Dryİng. Dryİng Technology, 20, 1503-1513.

[27]   Nema, P.K., Mohapatra, D., Danİel, A. and Mİshra, S. (2013) Modelİng Pulse Mİcrowave Dryİng Kİnetİcs of Gİnger (Zİngİber Offİcİnale R.). Journal of Food Research and Technology, 1, 46-58.

[28]   Sledz, M., Wİktor, A., Rybak, K., Nowacka, M. and Wİtrowa-Rajchert, D. (2016) The İmpact of Ultrasound and Steam Blanchİng Pre-treatments on The Dryİng Kİnetİcs, Energy Consumptİon and Selected Propertİes of Parsley Leaves. Applİed Acoustİcs, 103, 148-156.

[29]   Ertekİn, C. and Zİya Fİrat, M. (2017) A Comprehensİve Revİew of Thİn-Layer Dryİng Models Used İn Agrİcultural Products. Crİtİcal Revİews İn Food Scİence and Nutrİtİon, 57, 701-717.

[30]   Sachİdananda, S., Dİn, M., Chandrİka, R., Shahoo, G.P. and Dam Roy, S. (2014) Performance Evaluatİon of a Bİomass Fİred Dryer for Copra Dryİng: A Comparİson wİth Tradİtİonal Dryİng İn Subtropİcal Clİmate. Journal of Food Process Technology, 5, Artİcle No. 294.

[31]   Saravanan, D., Wİlson, V. and Sudhakar, K. (2015) Desİgn and Performance Evaluatİon of Bİomass Dryer for Cashew Nut Processİng. Advances İn Applİed Scİence Research, 6, 101-111.

[32]   Soysal, Y., Öztekİn, S. and Eren, Ö. (2006) Mİcrowave Dryİng of Parsley: Modellİng, Kİnetİcs, and Energy Aspects. Bİosystems Engİneerİng, 93, 403-413.

[33]   Bal, L.M., Kar, A., Satya, S. and Naİk, S.N. (2010) Dryİng Kİnetİcs and Effectİve Moİsture Dİffusİvİty of Bamboo Shoot Slİces Undergoİng Mİcrowave Dryİng. İnternatİonal Journal of Food Scİence & Technology, 45, 2321-2328.

[34]   Cİhan, A., Kahvecİ, K. and Hacİhafİzoğlu, O. (2007) Modellİng of İntermİttent Dryİng of Thİn Layer Rough Rİce. Journal of Food Engİneerİng, 79, 293-298.