Modeling the Thin‐Layer Drying of Fruits and Vegetables: A Review

Onwude, Daniel I.; Hashim, Norhashila; Janius, Rimfiel; Nawi, Nolila Mohd; Abdan, Khalina

doi:10.1111/1541-4337.12196

Cited by 411 publications

(313 citation statements)

References 92 publications

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“…In Fig. 1 the diffusion coefficient values are shown depending on the drying time that was calculated by formula (1): F 1 -taking one term (3) and F 2 -using ten terms of sum (1). Fig.…”

Section: Resultsmentioning

confidence: 99%

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Diffusion coefficient estimation difficulties at the beginning of drying experiment

Aboltins¹,

Rubina²,

Jotautienė³

2017

Engineering for Rural Development

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Abstract. The aim of this paper is to determine the error of the diffusion coefficient of removed moisture applying convection drying of carrot slices. In this study series for diffusion coefficient calculation are applied and the diffusion coefficient error at the beginning of the experiment is investigated. The experiments were carried out with carrot slices of 15 mm thickness on laboratory conditions. There are compared drying processes by different drying temperatures with the purpose to investigate the temperature effect on the diffusion coefficient changes. Using the experimental data the theoretical diffusion coefficient was calculated using iterative methods with small drying time. The research shows that one should be careful when using the first term of solution of the Fick's second law of diffusion, although in practice only the first term of the series is often applied. The results of this research showed that the error of the diffusion coefficient occured due to several members of series at small times. The theoretical results are useful for description and modelling of the drying process with the time dependent diffusion coefficient for carrot slices and pieces on two-and threedimensional case. The calculated parameters can be used for further research work and for improvement of the whole drying process.Keywords: drying, diffusion coefficient, carrot. IntroductionDrying of agricultural products is one of the main goals of preservation, transportation and commerce. The drying process reduces the moisture content in materials. It helps minimize microbiological activity in products during storage time and increases the product stability and persistence.One of the important aspects for understanding the drying process is to process mathematical modelling. The sgricultural production drying process is a complex process. To determine the drying rate and optimized drying parameters a suitable kinetic model is needed. There are many biological material drying kinetic models [1]. Most popular are the models of thin layer drying. These models allow many physical variables considered as constants and thus simplify the mathematical model of drying [2].On average, each of these models is based on the application of various mathematical expressions to describe the experimental data without the required parameter explanation and application description. Often in these cases the main criterion is the R 2 value without taking into account the measurement errors.On the other hand an important indicator of the drying process is the diffusion coefficient. This is particularly important for product drying, which cannot be regarded as a thin layer. Knowing it we are better able to simulate numerically the body drying. The drying coefficient and diffusion coefficient are connected with the solution of the Fick's second law of diffusion [3; 4]. However, in practice only the first term of the series is often applied and only for a long drying period [5].The purpose of this article is to clarify what error is made using th...

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“…In Fig. 1 the diffusion coefficient values are shown depending on the drying time that was calculated by formula (1): F 1 -taking one term (3) and F 2 -using ten terms of sum (1). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To determine the drying rate and optimized drying parameters a suitable kinetic model is needed. There are many biological material drying kinetic models [1]. Most popular are the models of thin layer drying.…”

mentioning

confidence: 99%

Diffusion coefficient estimation difficulties at the beginning of drying experiment

Aboltins¹,

Rubina²,

Jotautienė³

2017

Engineering for Rural Development

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“…Rumput laut coklat Sargassum sp. juga memiliki potensi yang luar biasa di samping merupakan sumber alginat yang dapat dimanfaatkan sebagai pengemulsi, penstabil dan pensuspensi (Kadi, 2005), serta sebagai sumber antioksidan (Norra, Aminah, & Suri, 2016). Berbagai manfaat tersebut menyebabkan kedua rumput laut memiliki nilai ekonomis.…”

Section: Pendahuluanunclassified

Karakteristik Pengeringan Rumput Laut Ulva sp. dan Sargassum sp.

Prasetyo

Jatmiko

Poeloengasih

2018

JPBKP

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Dalam penelitian ini karakteristik pengeringan dari rumput laut Ulva sp. dan Sargassum sp. telah dipelajari. Proses pengeringan dilakukan pada kondisi variasi suhu 40, 50, dan 60 °C di dalam alat pengering laboratorium. Laju pengeringan dievaluasi dengan empat model pengeringan lapis tipis, yakni Newton, Page, Two-Term, dan Midilli. Model yang paling sesuai ditentukan dari nilai sum square error (SSE) dan root mean square error (RMSE) terendah, serta nilai r tertinggi. Laju pengeringan kedua rumput laut memperlihatkan adanya periode laju pengeringan menurun dan tidak ada periode laju pengeringan konstan pada pengeringan Ulva sp. dan Sargassum sp. Hasil menunjukkan bahwa laju pengeringan meningkat seiring peningkatan kadar air dan suhu, dan laju pengeringan menurun seiring dengan berjalannya waktu. Laju pengeringan tertinggi diperoleh pada suhu 60 °C untuk Ulva sp. dan Sargassum sp. Hasil evaluasi menunjukkan bahwa model Midilli memiliki nilai SSE dan RMSE terendah, serta nilai r tertinggi. Berdasarkan hasil tersebut model Midilli merupakan model yang paling sesuai untuk menggambarkan laju pengeringan Ulva sp. dan Sargassum sp.

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“…Drying of agricultural products can be described by typical drying curves that illustrate the temperature, drying rate and moisture ratios as a function of time. These curves are divided into the constant rate, first falling rate and second falling rate periods as a function of time [15]. The nature of the manure and the moisture content determine the rate of diffusion through the porous media represented by the manure.…”

Section: Manure Drying Front and Water Lossmentioning

confidence: 99%