scholarly journals Thin-layer drying behavior of West Indian lemongrass (Cymbopogan citratus ) leaves

2018 ◽  
Vol 6 (4) ◽  
pp. 1085-1099 ◽  
Author(s):  
Saheeda Mujaffar ◽  
Sherida John
Keyword(s):  
Thin Layer ◽  
West Indian ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Cymbopogan Citratus

scholarly journals Thin layer drying behavior of Ginkgo biloba L. leaves with respect to Ginkgolide A and Bilobalide content and microbial load

Heliyon ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e03220
Author(s):  
Vasudha Agnihotri ◽  
Priyanka Adhikari ◽  
Neha Pandey ◽  
Priyanka Sati ◽  
Anita Pandey
Keyword(s):  
Thin Layer ◽  
Ginkgo Biloba ◽  
Microbial Load ◽  
Ginkgolide A ◽  
Thin Layer Drying ◽  
Drying Behavior

scholarly journals Cabya (Piper retrofractum Vahl) fruit under open sun drying: drying behavior and modeling of thin layer drying kinetics

2020 ◽  
Vol 542 ◽  
pp. 012001
Author(s):  
L C Hawa ◽  
Ubaidillah Ubaidillah ◽  
F N Afifah ◽  
N I W Yosika ◽  
A Nurlaily ◽  
...  
Keyword(s):  
Thin Layer ◽  
Drying Kinetics ◽  
Sun Drying ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Piper Retrofractum

Influence of Drying Conditions and Mathematical Models on the Thin-Layer Drying of Mushrooms

2014 ◽  
Author(s):  
A. Stegou-Sagia ◽  
D. V. Fragkou
Keyword(s):  
Mass Transfer ◽  
Thin Layer ◽  
Mathematical Models ◽  
Drying Methods ◽  
Suitable Model ◽  
Drying Time ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Drying Models ◽  
Drying Rates

In the present research, experimental data from several studies about drying behavior of mushrooms have been selected and used to compare different drying methods and different mathematical thin layer drying models to simulate mushroom drying rates. The white button (Agaricus Bisporus), the oyster (Pleurotus Ostreatus) and the milky mushroom slices have been considered for drying in different dryers such as hot air cabinet dryer and fluidized bed dryer with different slice thicknesses, drying air temperatures (45 °C to 90 °C) and drying air velocities (0.2 m/s to 5 m/s). The entire drying process has taken place in the falling rate period, assuming that internal mass transfer occurred by diffusion in mushroom slices. The study shows that the drying air temperature and the drying air velocity have an effect on the moisture removal from mushrooms and also on the drying time. Mathematical models have been proved to be useful for design and analysis of heat and mass transfer during drying processes. All the drying models considered in this study could adequately represent the thin layer drying behavior of mushrooms. Furthermore, as it is obvious, any type of mushrooms has its own most suitable model.

scholarly journals The Experimental Research of Drying Kinetics of Nagpur Orange Fruit (Citrus Sinesis- L) by Hot Air Electrical Dryer

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1131-1134
Keyword(s):  
Thin Layer ◽  
Drying Kinetics ◽  
Drying Time ◽  
Drying Characteristics ◽  
Drying Temperature ◽  
Hot Air ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Orange Fruit ◽  
Kinetics Of

The study is aimed experimentally and compared with the theoretical results of drying kinetics of Nagpur orange fruit dried in a hot air electrical dryer. Orange fruit is highly perishable and needs to be consumed or processed immediately after harvest. Drying or dehydration is one of the most practical methods of preserving food products. Therefore, thin layer drying characteristics of falling rate of Nagpur orange are determined experimentally under different conditions of drying air temperatures, relative humidity and air velocities for different moisture contents. Thin layer models like Wang and Singh, Page and Henderson have been compared with Experimental results. The knowledge of drying kinetics helps for identification of exact drying time and air flow velocity for different moisture content. Here drying operation is carried out at a velocity of 1m/sec and 1.25 m/sec for different temperature of 55°C, 65°C and 75°C. This analysis reveals that drying temperature has a more significant effect on moisture removal while velocity has the least effect. Drying rate is found to increase with the increase in drying temperature and reduce with drying time. Experimental data is statistically correlated by plotting the drying characteristics curve. The analysis reveals that Wang and Singh's model is a better model to explain the drying behavior of Nagpur Orange fruit (R2=0.9888).

scholarly journals Drying kinetics of dill leaves in a convective dryer

2013 ◽  
Vol 27 (1) ◽  
pp. 39-47 ◽  
Author(s):  
A. Motevali ◽  
S. Younji ◽  
R. Amiri Chayjan ◽  
N. Aghilinategh ◽  
A. Banakar
Keyword(s):  
Thin Layer ◽  
Back Propagation ◽  
Moisture Diffusivity ◽  
Moisture Ratio ◽  
Coefficient Of Determination ◽  
Feed Forward ◽  
Threshold Functions ◽  
Feed Forward Back Propagation ◽  
Thin Layer Drying ◽  
Drying Behavior

Abstract Thin layer drying characteristics of dill leaves under fixed, semi-fluidized, and fluidized bed conditions were studied at air temperatures of 30, 40, 50, and 60°C. In order to find a suitable drying curve, 12 thin layer-drying models were fitted to the experimental data of the moisture ratio. Among the applied mathematical models, the Midilli et al. model was the best for drying behavior prediction in thin layer drying of dill leaves. To obtain the optimum network for drying of dill leaves, various numbers of multilayer feed-forward neural networks were made and tested with different numbers of hidden layers and neurons. The best neural network feed-forward back-propagation topology for the prediction of drying of dill leaves (moisture ratio and drying rate) was the 3-45-2 structure with the training algorithm trainlm and threshold functions logsig and purelin. The coefficient of determination for this topology for training, validation, and testing patterns was 0.9998, 0.9981, and 0.9990, respectively. Effective moisture diffusivity of dill leaves during the drying process in different bed types was found to be in the range from 7.10 10-12 to 1.62 10-10 m2 s-1. Also, the values of activation energy were determined to be between 75.435 and 80.118 kJ mol-1

scholarly journals Thin layer drying behaviour of fermented cocoa (Theobroma cacao L.) beans

2018 ◽  
Author(s):  
Saheeda Mujaffar ◽  
Aveena Ramroop ◽  
Darin Sukha
Keyword(s):  
Thin Layer ◽  
Rest Period ◽  
Effective Diffusivity ◽  
Constant Diffusion Coefficient ◽  
Constant Diffusion ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Drying Behaviour ◽  
Drying Curves ◽  
The Impact

To assess the impact of a rest interval and bean turning on the thin-layer drying behavior of fermented cocoa beans, beans of mixed Trinitario varieties were dried in a cabinet oven at three temperatures (40, 50, 60°C) using three drying regimes, namely; continuous drying, intermittent drying (drying for 8h with a rest period of 16h), and intermittent drying with turning of beans.  Moisture content, water activity, pH and colour attributes were measured and sensory evaluation of the cocoa liquor carried out on selected samples.  Drying curves were constructed and drying rate constants (k) and effective diffusivity (Deff) values determined. Keywords: Oven-drying; Fick’s Law; Rate constant; Diffusion coefficient

scholarly journals Thin Layer Drying and Modelling of Poultry Litter Briquettes

2019 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Mogomotsi J. Molefe ◽  
Isaac N. Simate
Keyword(s):  
Thin Layer ◽  
Poultry Litter ◽  
Statistical Parameters ◽  
Tunnel Dryer ◽  
Thin Layer Drying ◽  
Chi Squared ◽  
Drying Behavior ◽  
Energy Consuming ◽  
Drying Curves ◽  
Microsoft Office

Drying is an energy consuming process influenced by humidity, air velocity and temperature and is defined as a heat conveyance process wherein the product is heated hence removing moisture. Thin layer drying equations are used to estimate drying times of products and generalizing their drying curves. In this study, mathematical modelling and prediction of drying behavior of poultry litter briquettes (PLB) was investigated through open sun drying (OSD) and solar tunnel drying for moisture content (MC) calculations. A solar tunnel dryer (STD) having a: black painted collector unit, drying unit and black painted vertical bare flat-plate chimney was used. MC results were converted to moisture ratio and fitted into 12 different thin layer drying models, using Microsoft Office Excel, which were compared according to their coefficients of determination to estimate drying curves of PLB. The most accurate model was selected based on three statistical parameters: correlation coefficient (R2), chi-squared (χ2) and Root Mean Square Error (RMSE). Solar insolation of between 220 and 1005 W/m2 resulted in air temperature of up to 64oC at the collector unit, up to 60oC at the drying unit and an ambient temperature of up to 31oC. Exposure of PLB with an average initial MC of 61% (w.b.) to these conditions resulted in a final MC in a range of 0.2-11.2% (w.b.) in 31-55 hours. PLB was dried to similar final weight from whichever drying method although OSD took longer than STD. The Logarithmic model was found to satisfactorily describe the drying curves of PLB with R2 of 9.93E-01-9.99E-01; χ2 of 1.36E-11-6.50E-14; and RMSE of 2.94E-02-1.30E-02.

Experimental Study of Thin Layer Drying Behavior of a Fish

2014 ◽  
pp. 681-686
Author(s):  
Murat Erdem ◽  
Yasin Varol ◽  
Hakan Fehmi Öztop ◽  
Fethi Kamışlı
Keyword(s):  
Experimental Study ◽  
Thin Layer ◽  
Thin Layer Drying ◽  
Drying Behavior

scholarly journals  Modeling some drying characteristics of garlic sheets under semi fluidized and fluidized bed conditions

2012 ◽  
Vol 58 (No. 2) ◽  
pp. 73-82 ◽  
Author(s):  
R. Amiri Chayjan ◽  
K. Salari ◽  
B. Shadidi
Keyword(s):  
Thin Layer ◽  
Fluidized Bed ◽  
Air Temperature ◽  
Specific Energy Consumption ◽  
Initial Moisture Content ◽  
Convective Drying ◽  
Air Temperatures ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Drying Properties

Thin layer drying properties of high moisture garlic sheets under semi fluidized and fluidized bed conditions with high initial moisture content (about 154.26% d.b.) were studied. Air temperatures of 50, 60, 70 and 80&deg;C were applied to garlic samples. Among the applied models, Page model was the best to predict the thin layer drying behavior of garlic sheets. Using this model, correlation coefficient (R<sup>2</sup>) was high for all drying cases. The computed values of D<sub>eff</sub> were between 3.38 &times; 10<sup>&ndash;10</sup> and 2.54 &times; 10<sup>&ndash;9</sup> m<sup>2</sup>/s during the falling rate drying. Values of D<sub>eff</sub> for garlic sheets were also increased with increasing in input air temperature. Activation energy values were varied between 51.32 and 60.58 kJ/mol for 50 to 80&deg;C, respectively. The specific energy consumption (SEC) for garlic specimens was placed in the range of 0.316 &times; 10<sup>6</sup> and 0.979 &times; 10<sup>6</sup> kJ/kg from 50 to 80&deg;C, respectively. An increase in air temperature caused decrease in SEC value. Application of semi fluidized bed convective drying with temperature between 50 and 60&deg;C was suitable to produce dried garlic. &nbsp; &nbsp;

scholarly journals Statistical prediction of the drying behavior of blanched ginger rhizomes

2021 ◽  
Vol 4 (2) ◽  
pp. 98-107
Author(s):  
A. I. Gbasouzor ◽  
J. E. Dara ◽  
C. O. Mgbemena
Keyword(s):  
Linear Regression ◽  
Moisture Content ◽  
Thin Layer ◽  
Goodness Of Fit ◽  
Drying Time ◽  
Thin Layer Drying ◽  
Non Linear ◽  
Drying Behavior ◽  
Drying Models ◽  
Thin Layer Drying Models

ARS-680 environmental chamber was employed in this study to determine the drying behavior of sliced ginger rhizomes. Blanched and unblanched treated ginger rhizomes were considered at drying temperature of 40 °C for a period of 2 – 24 h. Linear and non-linear regression analyses were employed to establish the correlation that exits between the drying time and the moisture ratio. Correlation analysis, root mean square error (RMSE) and standard error of estimate (SEE) analysis were chosen in selecting the best thin layer drying models. Higher values of determination coefficient (R2) show goodness of fit and lower values of SEE implies better correlation; and RMSE values were also utilized in determining the goodness of fit. The drying data of the variously treated ginger samples were fitted into the twelve thin layer drying models and the data obtained were fitted by multiple non-linear regression technique. Blanched treated sample exhibited a better drying behavior losing about 82.87 % moisture content compared with unbleached sample that lost about 62.03 % of moisture content. Two-term exponential drying model proved to be the most suitable model for predicting the drying behavior of ginger rhizome. The model exhibited high R2 values of 0.9349-0.9792 (which are close to unity) for both blanched and unbleached samples. Also, it recorded relatively low values of RMSE and SEE (3.6865 - 2.0896 and 3.6564-2.7486 respectively) for both treatments.  

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