scholarly journals Moisture diffusion coefficient estimation in peas drying by means of a modified Hawlader and Uddin method

2018 ◽  
Author(s):  
Carlos Martínez-Vera ◽  
Mario Vizcarra-Mendoza
Keyword(s):  
Diffusion Coefficient ◽  
Diffusion Equation ◽  
Diffusion Coefficients ◽  
Moisture Diffusion ◽  
Moisture Diffusivity ◽  
Second Step ◽  
Drying Process ◽  
Moisture Diffusion Coefficient ◽  
Coefficient Estimation ◽  
Constant Diffusion

The aim of the present work is to determine the moisture diffusion coefficient in peas applying, in a first step, a methodology previously published in the literature by Uddin et al.[1] for determining constant diffusion coefficients taking in account the volume reduction associated to the drying process. Then, in a second step, refine it by means of an optimization step. The optimization step is justified because the methodology of Uddin et al. is based in a solution of the diffusion equation that is not mathematically valid for the drying-shrinking problem. Keywords: : moisture diffusivity; drying-shrinking; peas drying 

Drying Model and Moisture Diffusion Coefficient

2012 ◽  
Vol 472-475 ◽  
pp. 519-525
Author(s):  
Xian Xi Liu ◽  
Jun Ruo Che ◽  
Sai Zhang
Keyword(s):  
Diffusion Coefficient ◽  
Moisture Content ◽  
Diffusion Equation ◽  
Equilibrium Moisture Content ◽  
Initial Moisture Content ◽  
Moisture Diffusion ◽  
Sample Thickness ◽  
Moisture Distribution ◽  
Second Law ◽  
Moisture Diffusion Coefficient

Varied values of moisture diffusivity estimated using Crank’s equation with different initial moisture content, equilibrium moisture content and sample thickness are often reported. However, a theoretical explanation to this phenomenon is not available to date. To explore the possible reason of this phenomenon, a Fick’s second law diffusion equation for drying samples assumed uniform initial moisture distribution and negligible external resistance is solved numerically and the solutions as drying data is used to estimate the moisture diffusion coefficient of the sample through the equation reported by Crank. The result shows the Crank’s equation used to estimate moisture diffusion coefficient could not be theoretical solution of the Fick’s second law diffusion equation and the estimated value of moisture diffusion changing with initial moisture content, equilibrium moisture content and sample thickness perhaps caused by the Crank’s equation itself.

scholarly journals Modeling of mass transfer performance of hot-air drying of sweet potato (Ipomoea Batatas L.) slices

2014 ◽  
Vol 20 (2) ◽  
pp. 171-181 ◽  
Author(s):  
Aishi Zhu ◽  
Feiyan Jiang
Keyword(s):  
Diffusion Coefficient ◽  
Sweet Potato ◽  
Moisture Diffusion ◽  
Drying Process ◽  
Air Velocity ◽  
Potato Slice ◽  
Moisture Diffusion Coefficient ◽  
Hot Air ◽  
Effective Moisture ◽  
Air Dryer

In order to investigate the transfer characteristics of the sweet potato drying process, a laboratory convective hot air dryer was applied to study the influences of drying temperature, hot air velocity and thickness of sweet potato slice on the drying process. The experimental data of moisture ratio of sweet potato slices were used to fit the mathematical models, and the effective diffusion coefficients were calculated. The result showed that temperature, velocity and thickness influenced the drying process significantly. The Logarithmic model showed the best fit to experimental drying data for temperature and the Wang and Singh model were found to be the most satisfactory for velocity and thickness. It was also found that, with the increase of temperature from 60 to 80?C, the effective moisture diffusion coefficient varied from 2.962?10-10 to 4.694?10-10 m2?s-1, and it fitted the Arrhenius equation, the activation energy was 23.29 kJ?mol-1; with the increase of hot air velocity from 0.423 to 1.120 m?s-1, the values of effective moisture diffusion coefficient varied from 2.877?10-10 to 3.760?10-10 m2?s-1; with the increase of thickness of sweet potato slice from 0.002 m to 0.004 m, the values of effective moisture diffusion coefficient varied from 3.887?10-10 to 1.225?10-9 m2?s-1.

scholarly journals Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 260 ◽  
Author(s):  
Radosław Winiczenko ◽  
Krzysztof Górnicki ◽  
Agnieszka Kaleta
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Coefficient ◽  
Biot Number ◽  
Moisture Diffusion ◽  
Absolute Error ◽  
Mass Diffusion ◽  
Precise Determination ◽  
Nsga Ii ◽  
Moisture Diffusion Coefficient ◽  
Nondominated Sorting

A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s2 + 0.000024316T2 − 0.12478256sT, D = 1.27547936∙10−7 − 2.3808∙10−5s − 5.08365633∙10−9T + 0.0030005179s2 + 4.266495∙10−11T2 + 8.33633∙10−7sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s2 + 0.000024316T2 − 0.12478252sT, D = 1.27547948∙10−7 − 2.3806∙10−5s − 5.08365753∙10−9T + 0.0030005175s2 + 4.266493∙10−11T2 + 8.336334∙10−7sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

scholarly journals Kinetic Studies and Moisture Diffusivity During Cocoa Bean Roasting

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 770 ◽  
Author(s):  
Leydy Ariana Domínguez-Pérez ◽  
Ignacio Concepción-Brindis ◽  
Laura Mercedes Lagunes-Gálvez ◽  
Juan Barajas-Fernández ◽  
Facundo Joaquín Márquez-Rocha ◽  
...  
Keyword(s):  
Diffusion Coefficients ◽  
Kinetic Studies ◽  
Moisture Diffusion ◽  
Moisture Diffusivity ◽  
Cocoa Bean ◽  
Exponential Models ◽  
First Order ◽  
Roasting Process ◽  
Pseudo First Order ◽  
Semi Empirical

Cocoa bean roasting allows for reactions to occur between the characteristic aroma and taste precursors that are involved in the sensory perception of chocolate and cocoa by-products. This work evaluates the moisture kinetics of cocoa beans during the roasting process by applying empirical and semi-empirical exponential models. Four roasting temperatures (100, 140, 180, and 220 °C) were used in a cylindrically designed toaster. Three reaction kinetics were tested (pseudo zero order, pseudo first order, and second order), along with 10 exponential models (Newton, Page, Henderson and Pabis, Logarithmic, Two-Term, Midilli, Verma, Diffusion Approximation, Silva, and Peleg). The Fick equation was applied to estimate the diffusion coefficients. The dependence on the activation energy for the moisture diffusion process was described by the Arrhenius equation. The kinetic parameters and exponential models were estimated by non-linear regression. The models with better reproducibility were the pseudo first order, the Page, and the Verma models (R2 ≥ 0.98). The diffusion coefficients that were calculated were in the order of 1.26 to 5.70 × 109 m s−2 and the energy activation for moisture diffusion obtained was 19.52 kJ mol−1.

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