scholarly journals Some Aspects of the Modelling of Thin-Layer Drying of Sawdust

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 726
Author(s):  
Andrzej Bryś ◽  
Agnieszka Kaleta ◽  
Krzysztof Górnicki ◽  
Szymon Głowacki ◽  
Weronika Tulej ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Thin Layer ◽  
Air Temperature ◽  
Mean Square ◽  
Drying Process ◽  
Airflow Velocity ◽  
Chi Square ◽  
Drying Characteristics ◽  
Thin Layer Drying ◽  
Drying Models

Drying of spruce, beech, willow, and alder sawdust was examined in a laboratory type dryer. The effect of drying air temperature T (25, 60, and 80 °C) and airflow velocity v (0.01, 0.15, and 1.5 m/s) was investigated. The obtained results demonstrated that drying air temperature and airflow velocity have impacts on the drying of sawdust. The experimental dehydration data of sawdust obtained were fitted to theoretical, semi-theoretical, and empirical thin-layer models. The accuracies of the models were estimated using the correlation coefficient (R), root mean square error (RMSE), and reduced chi-square (χ2). All models except the theoretical model of a sphere described the drying characteristics of sawdust satisfactorily. The effect of T and v on the parameters (constants and coefficients) of the drying models were determined. The effect, by the proposed equations, was also described. This work combines aspects of mechanical engineering and modelling of the drying process.

scholarly journals Thin-layer drying of sawdust mixture

2016 ◽  
Vol 18 (4) ◽  
pp. 65-70 ◽  
Author(s):  
Krzysztof Górnicki ◽  
Agnieszka Kaleta ◽  
Andrzej Bryś ◽  
Radosław Winiczenko
Keyword(s):  
Theoretical Model ◽  
Thin Layer ◽  
Infinite Plane ◽  
Chi Square ◽  
Drying Characteristics ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Drying Behaviour ◽  
Effect Of The Composition ◽  
Better Than

Abstract Drying behaviour of sawdust mixture was investigated in a convective dryer at 0.01 m/s and 25, 60, and 150°C air temperature. Sawdust mixture (60% of spruce and 40% of the second ingredient: beech, willow, ash, alder) and sawdust of spruce, beech, willow, alder and ash was used in the drying experiments. The sawdust mixture drying was affected by the drying of its ingredients. The experimental drying data were fitted to the theoretical, semi–theoretical, and empirical thin-layer models. The accuracies of the models were measured using the correlation coefficient, root mean square error, and reduced chi–square. All semi-theoretical and empirical models described the drying characteristics of sawdust mixture satisfactorily. The theoretical model of a sphere predicts the drying of sawdust mixture better than the theoretical model of an infinite plane. The effect of the composition of the sawdust mixture on the drying models parameters were also taken into account.

Thin Layer Drying Process of Some Leafy Vegetables under Open Sun

2007 ◽  
Vol 13 (1) ◽  
pp. 35-40 ◽  
Author(s):  
O. P. Sobukola ◽  
O. U. Dairo ◽  
L. O. Sanni ◽  
A. V. Odunewu ◽  
B. O. Fafiolu
Keyword(s):  
Thin Layer ◽  
Effective Diffusivity ◽  
Thin Layers ◽  
Leafy Vegetables ◽  
Drying Process ◽  
Chi Square ◽  
Thin Layer Drying ◽  
Rate Period ◽  
Drying Models ◽  
Drying Curves

Open sun drying experiments in thin layers of crain-crain (CC), fever (FV) and bitter (BT) leaves grown in Abeokuta, Nigeria were conducted. The drying process took place in the falling rate period and no constant rate period was observed from the drying curves. Eight thin layer mathematical drying models were compared using the multiple determination coefficients (R2), reduced chi-square (χ2) and root mean square error (RMSE) between the observed and predicted moisture ratios. Accordingly, Midilli et al. model satisfactorily described the drying curves of the three leaves with R2 of 0.9980, χ2 of 2.0×10-4 and RMSE of 1.09×10-2 for CC leaves; R2 of 0.9999, χ2 of 2×10-6 and RMSE of 1.11×10-3 for FV leaves; and R2 of 0.9998, χ2 of 1.9×10-5 and RMSE of 3.3×10-3 for BT leaves. The effective diffusivity was found to be 52.91×10-10, 48.72×10-10 and 43.42×10-10 m2/s for CC, BT and FV leaves, respectively.

scholarly journals Thin layer drying of zucchini in solar dryer located in Osmaniye region

2018 ◽  
Vol 12 (2) ◽  
pp. 79-85 ◽  
Author(s):  
Kamil Neyfel Çerçi ◽  
Özge Sufer
Keyword(s):  
Thin Layer ◽  
Linear Regression Analysis ◽  
Chi Square ◽  
Mathematical Expressions ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Drying System ◽  
Thin Layer Drying Models ◽  
Dehydration Behavior ◽  
And Diffusion

In this study, the dehydration behavior of zucchini using solar assisted drying system was examined according to 22 thin layer drying models available in literature. The correlation coefficient (R2), chi-square (χ2) and root mean square error (RMSE) values were calculated to check the suitability of models by non-linear regression analysis. It was found that Cubic and Modified Midilli-1 models were the most suitable equations and their R2 values were calculated as 0.99963. χ2 and RMSE values of related mathematical expressions were 1.89343×10‒5, 1.91692×10‒5 and 0.01685×10‒3, 0.01721×10‒3 respectively. In addition, heat transfer, mass transfer and diffusion coefficients, which were important parameters in design of drying systems were also determined as 5.18124 W/m2°C, 1.57129×10‒7 m/s and 2.335718×10‒9 m2/s respectively.

Drying Process Modelling for Heavy Clay Products Using a New Thin Layer Drying Model

2013 ◽  
Vol 371 ◽  
pp. 323-327
Author(s):  
Miloš Vasić ◽  
Zagorka Radojević
Keyword(s):  
Thin Layer ◽  
Mathematical Models ◽  
Process Modelling ◽  
Experimental Investigations ◽  
Drying Process ◽  
Thin Layer Drying ◽  
Heavy Clay ◽  
Drying Models ◽  
Drying Model ◽  
Thin Layer Drying Models

Drying results, determined on samples made of masonry clay from the locality "Banatski Karlovac", are presented in this study. Experimental investigations were carried out in a laboratory recirculation dryer in which drying parameters (humidity, temperature, and velocity) could be programmed, controlled and monitored during drying process. Several mathematical models were used for drying process modelling. New semi-theoretical thin layer drying model, for heavy clay products, was developed and presented in this study. It represents a modification of Page's and logarithm's thin layer drying models. Results presented in this study have shown that new thin layer drying model describes and correlates the best experimentally determined drying process.

scholarly journals Modelling and characteristics of thin layer convective air-drying of thyme (Thymus vulgaris) leaves

2019 ◽  
Vol 37 (No. 2) ◽  
pp. 128-134
Author(s):  
Osman Yağız Turan ◽  
Ebru Fıratlıgil
Keyword(s):  
Thin Layer ◽  
Goodness Of Fit ◽  
Food Preservation ◽  
Moisture Ratio ◽  
Coefficient Of Determination ◽  
Thymus Vulgaris ◽  
Drying Process ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Kinetics Of

Fruit and vegetable dehydration has been extensively studied for the improvement of food preservation. Effects of drying temperature on the drying kinetics of thyme were investigated and a suitable drying model was obtained to describe the drying process. Drying behaviour of thyme leaves at temperatures of 50, 60, 70 and 80°C was determined by using a conventional drying oven, and moisture ratio and drying rates were calculated. Four different thin layer drying models, namely Lewis, Henderson and Pabis, Page, and logarithmic models, were used to fit the experimental moisture ratio data. Three statistical parameters: coefficient of determination (R<sup>2</sup>), chi-square (χ<sup>2</sup>) and root mean square error (RMSE) were used to compare the goodness of fit of the drying models. Logarithmic model and Page model give the best description of the drying process kinetics of thyme leaves by comparing the experimental values and predicted values.

Simulation of microwave thin layer drying process by a new theoretical model

2017 ◽  
Vol 162 ◽  
pp. 69-76 ◽  
Author(s):  
Jun Jiang ◽  
Leping Dang ◽  
Cheong Yuensin ◽  
Hongsing Tan ◽  
Bochen Pan ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Thin Layer ◽  
Drying Process ◽  
Thin Layer Drying

scholarly journals Thin-Layer Drying Characteristics and Modeling of Chinese Jujubes

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Xiao-Kang Yi ◽  
Wen-Fu Wu ◽  
Ya-Qiu Zhang ◽  
Jun-Xing Li ◽  
Hua-Ping Luo
Keyword(s):  
Thin Layer ◽  
Distribution Model ◽  
Drying Time ◽  
Drying Characteristics ◽  
Simulation And Optimization ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Higher Temperature ◽  
Drying Rates ◽  
Experimental Values

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.

Evaluation of Thin-Layer Drying Models and Artificial Neural Networks for Describing Drying Kinetics of Canola Seed in a Heat Pump Assisted Fluidized Bed Dryer

2013 ◽  
Vol 9 (4) ◽  
pp. 375-384 ◽  
Author(s):  
Narjes Malekjani ◽  
Seid Mahdi Jafari ◽  
Mohammad Hashem Rahmati ◽  
Ebrahim Esmaeel Zadeh ◽  
Hbibollah Mirzaee
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Thin Layer ◽  
Artificial Neural Network Model ◽  
Mean Square ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Thin Layer Drying Models ◽  
Artificial Neural

AbstractIn this study, drying characteristics of canola seeds were determined using heated ambient air at 40, 50 and 60°C, relative humidity of 20, 40 and 60% and constant velocity of 3 m/s. To select a suitable drying curve, six thin-layer drying models were fitted to experimental data. The models were compared according to three statistical parameters: R2, reduced chi-square (χ2) and root mean square error. Using some experimental data, an Artificial neural network model, trained by Feed Forward Back-Propagation algorithm, was developed to predict moisture ratio values based on the three input variables. Different activation functions and several rules were used to assess percentage error between the desired and predicted values. According to the results, the approximation of diffusion drying model had better agreement with the drying data. The artificial neural network model was able to predict the moisture ratio quite well with R2 of 0.9994. The predicted mean square error was obtained as 0.00012575.

scholarly journals Influence of temperature and thickness on thin layer drying characteristics of onion (Allium cepa L.) varieties and rehydration capacity

2020 ◽  
Vol 12 (2) ◽  
pp. 165-176
Author(s):  
Sunday Samuel Sobowale ◽  
Oluwole Benjamin Omotoso ◽  
Yusuf Olamide Kewuyemi ◽  
Olawale Paul Olatidoye
Keyword(s):  
Thin Layer ◽  
Moisture Diffusivity ◽  
Quality Characteristics ◽  
Nonlinear Regression Analysis ◽  
Rehydration Ratio ◽  
Drying Characteristics ◽  
Allium Cepa L ◽  
Thin Layer Drying ◽  
Drying Models

Nonlinear regression analysis was conducted for thin layer drying characteristics of two onion varieties (white and red) and some quality characteristics were also examined. The experimental data obtained at drying temperatures of 40, 50, and 60 oC and thicknesses of 2, 4 and 6 mm, was subsequently fitted into four commonly used models (Henderson and Pabis, Lewis, Page, and logarithmic). Moisture diffusivity and activation energy ranged from 8.9 × 10-10 to 8.4 × 10-9 m2/s and 55.98 to 65.68 KJ/mol, respectively. Significant differences (p&lt;0.05) were observed in the colour profile and rehydration ratio. The optimum desirable colour was obtained at 50 oC with 2 mm thick onion slices and the observed higher rehydration ratio indicates good quality of dried onions. Among the four selected drying models, the Page model predicted optimally (R2&gt; 0.9) and was found to be better in describing dried onion varieties, while the Lewis model provided the least fit.

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