Development of a Visual Method to Test the Range of Applicability of Thin Layer Drying Equations Using MATLAB Tools

Wang, Dai-Chyi; Fon, Din-Sue; Fang, Wei; Sokhansanj, Shahab

doi:10.1081/drt-200032878

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…Thin layer drying is generally defined as the practical drying of one layer of particles or samples (Akhondi, Kazemi, & Maghsoodi, 2011). Thin-layer drying equation representing moisture exchange between the thin layer of the product and its surrounding air during the process is fundamental to the drying simulation (Wang, Fon, Fang, & Sokhansanj, 2004).…”

Section: Thin-layer Drying Equationsmentioning

confidence: 99%

“…Thin layer drying is generally defined as the practical drying of one layer of particles or samples (Akhondi, Kazemi, & Maghsoodi, ). Thin‐layer drying equation representing moisture exchange between the thin layer of the product and its surrounding air during the process is fundamental to the drying simulation (Wang, Fon, Fang, & Sokhansanj, ). Thin‐layer drying models are aimed at describing the drying phenomena, estimation of drying times, and access to drying curves for several products, which are available in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Sadeghi

Movagharnejad

Asl

2019

J Food Process Preserv

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A comparative study was carried out between a large number of mathematical models and artificial neural networks to estimate the drying curves. Diamente et al. model and modified two‐term exponential‐V model were determined as the best ones describing drying curves for natural and forced drying air systems, respectively. The ANNs with 4‐9‐9‐1 and 3‐9‐1 topologies, log‐sigmoid, and hyperbolic tangent sigmoid transfer functions, and Levenberg–Marquardt training algorithm presented the best results for the former and latter systems, respectively. Furthermore, it was found that ANN modeling had much better performance in prediction of drying curves with respect to statistical analyses. Moisture diffusivity values were obtained in a range of 0.932×10-10-11.976×10-10normalm2/s and 2.209×10-10-9.848×10-10normalm2/s for the systems, respectively. Activation energies were determined as 88,509, 60,344, and 44,806 W/kg for the former system and 36.88, 29.66, and 18.59 kJ/mol for the latter system with an increase in the thickness of samples. Practical applications The main aim of drying food products is the reduction of moisture content up to a certain level to achieve the smallest possible amount of the microbiological spoilage. This process has a considerable effect on the drying kinetics and quality of the dried product as well. Infrared (IR) heating, as an alternative drying method for agricultural products, is efficient to preserve the main characteristics and to shorten drying time. It is important to investigate the effect operating parameters of IR dryer on the mentioned cases. Our studies have clearly displayed that IR heating of pumpkin samples can result in high heating rate and fast drying. Therefore, this alternative approach could be employed as an energy saving drying method along with improved drying efficiency and better product quality in comparison with the common drying methods. Study results are useful for producers of not only dried pumpkins but also other agricultural products.

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Section: Thin-layer Drying Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of infrared radiation thin‐layer drying of pumpkin samples under natural and forced convection

Sadeghi

Movagharnejad

Asl

2019

J Food Process Preserv

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“…Recently, Wang et al [17] have given a comprehensive set of correlations for thin-layer drying data using MATLAB.…”

Section: Analysis Of Withering Datamentioning

confidence: 99%

Mathematical Modeling of Withering Characteristics of Tea Leaves

2006

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The withering characteristics of tea leaves were examined for different temperatures. Tea leaves were withered at a temperature range of 20-45 C with a constant air velocity of 1.1 m/s. The experimental results illustrated the absence of constant-rate drying period and withering took place only in the falling-rate period. During the falling-rate period, at constant drying air flow rate, the drying rate increased and drying time decreased with the increase in drying air temperature. Drying models of Henderson and Pabis and Page were evaluated based on mean bias error (E MB ), root mean square error (E RMS ), correlation coefficient (R 2 ), and the chi square (v 2 ). The Henderson and Pabis model was found to be a better model for describing the withering characteristics of tea leaves for each of the temperatures of 20, 25, 30, and 35 C. The values obtained from Page model were found to be more reasonable for temperatures of 40 and 45 C than the other model. Both the models closely fitted the withering data within a certain range of temperature. The Henderson and Pabis model gave better prediction and satisfactorily described the withering characteristics of tea leaves at temperatures lower than 40 C whereas the Page model fitted well at temperatures greater than 40 C.

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“…TLDRY software was found to be best and convenient tool for the Newton and Page model [15]. Seginera and Bux were used MATLAB to develop a drying rate model for waste water sludge for greenhouse dryer [16].…”

Section: Matlab Simulationmentioning

confidence: 99%