scholarly journals Approximation of effective moisture-diffusion coefficient to characterize performance of a barrier coating

2013 ◽  
Vol 114 (17) ◽  
pp. 174302 ◽  
Author(s):  
Shingo Nagai
Keyword(s):  
Diffusion Coefficient ◽  
Moisture Diffusion ◽  
Moisture Diffusion Coefficient ◽  
Effective Moisture ◽  
Barrier Coating

scholarly journals Moisture transport through non-porous hydrophilic membranes used in protective clothing

2013 ◽  
Vol 17 (5) ◽  
pp. 1293-1298 ◽  
Author(s):  
Fang-Long Zhu ◽  
Yu Zhou ◽  
Jian-Xin He
Keyword(s):  
Diffusion Coefficient ◽  
Diffusion Model ◽  
Transfer Process ◽  
Moisture Transfer ◽  
Effective Diffusion ◽  
Moisture Diffusion ◽  
Mass Transfer Process ◽  
Transfer Resistance ◽  
Moisture Diffusion Coefficient ◽  
Effective Moisture

The three-step upright cup method was employed to determine the total moisture transfer resistance and the two air layer resistances on both sides of the membrane. The effective moisture diffusion coefficient in air layer between the membrane and water surface was determined by the regressive method, and the effective moisture diffusion coefficient of membrane was calculated. Experiments were conducted on a non-porous hydrophilic thermoplastic polyester elastomer membrane. The moisture transfer process through the membrane was modeled by using the solution-diffusion model. The effects of membrane microstructure on membrane permeation were analyzed based on the solution-diffusion model and experimental data. The results show that the effective diffusion coefficient can be used to evaluate the mass transfer process through the non-porous hydrophilic thermoplastic polyester elastomer membrane.

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.

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