Simulation and Validation of Thin Layer Models for Peanut Drying

Yang, Changjo; Fon, Din-Sue; Lin, Ta‐Te

doi:10.1080/07373930701537278

Cited by 18 publications

(6 citation statements)

References 12 publications

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“…Therefore, the heat and mass transfer of peanut pods are complex; thus, it is difficult to construct a drying model based on the characteristics of each component. In most of the recent studies, peanut pods are regarded as cylindrical entities, and the classic semi-empirical thin-layer drying equations are generally fitted to the experimental data [18,30] .…”

Section: Principle Of Reversing Ventilation Dryingmentioning

confidence: 99%

“…Chai and Young [17] studied the effects of airflow rate on drying times and costs of conventional and recirculating peanut drying facilities using numerical simulation. Yang et al [18] studied the simulation of peanut drying in a trailer-type dryer with different thin-layer drying models and equilibrium moisture contents. The result showed that the Henderson-Pabis model using the Hummeida K-value and modified Oswin EMC models provided the best fit with the experimental data, although errors of temperature prediction existed in the middle layer.…”

Section: Introduction mentioning

confidence: 99%

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Optimizing the drying parameters of a fixed bed with reversing ventilation for peanut using computer simulation

Jin

Xie

Wei

et al. 2021

International Journal of Agricultural and Biological Engineering

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To obtain the optimal operation parameters of fixed-bed reversing ventilation drying of peanuts, a set of partial differential equations indicating the heat and mass transfer relationships between the peanut pods and air during drying was proposed. Then, a series of discretized models were established for simulation, and the time consumed, unevenness, and energy consumption for batch drying were calculated. The results showed that reversing ventilation and segmented drying was helpful to these issues for high drying ability. The optimal operation parameters were determined by uniform design experimentation of mathematical simulation. The result showed that when the moisture content (wet basis) was above 22%, a ventilation velocity of 0.46 m/s was optimal; when the moisture content was between 8% and 22%, a ventilation velocity of 0.20 m/s was optimal. Using the optimal parameters, the computer simulating result was compared with the experimental results. The correlation coefficients between the simulating and the experimental values for the temperature and moisture content were all above 0.98 and the quality of dried peanuts was close to that of natural sun-dried ones, which indicates that the optimization results of the drying parameters are highly reliable.

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Section: Principle Of Reversing Ventilation Dryingmentioning

confidence: 99%

Section: Introduction mentioning

confidence: 99%

Optimizing the drying parameters of a fixed bed with reversing ventilation for peanut using computer simulation

Jin

Xie

Wei

et al. 2021

International Journal of Agricultural and Biological Engineering

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“…If the wet peanut was not dried in time, it was susceptible to mildew. Some molds in the wet peanut may (especially Aspergillus flavus ) produce aflatoxins, which was proven potent carcinogens in livestock and humans (Ertas et al, 1999; Lewis, Trabelsi, & Nelson, 2017a; Lewis, Trabelsi, & Nelson, 2017b; Yang, Fon, & Lin, 2007). Hence, peanut should be dried to moisture content 10% or below for its proper storage and processing (Allgood, Vellidis, Allison, Perry, & Kvien, 1993; Lewis, Trabelsi, Nelson, Tollner, & Haidekker, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…However, sun dried method was usually influenced by rain, dust, insects, rodents and other factors (Diamante & Munro, 1993). The convection drying was an effective way to dry the peanut quickly and prevent the peanut from being affected by other environmental and biological factors (Polat, Taskin, Izli, & Asik, 2019; Yang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Goneli et al (2017) studied these mathematical models with peanut kernels drying in thin layer. Yang et al (2007) also employed Hummeida K ‐value model and modified Oswin EMC model to study the thin layer drying of peanut, which showed good prediction. Furthermore, artificial neural network (ANN) had successfully described the drying kinetics of rice (Alam et al, 2018; Beigi, Torki‐Harchegani, & Tohidi, 2017), mosambi peel (Chaurasia, Younis, Qadri, Srivastava, & Osama, 2018) and quince (Taghinezhad, Kaveh, Jahanbakhshi, & Golpour, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A moisture content prediction model for deep bed peanut drying using support vector regression

Wang

Jin

et al. 2020

J Food Process Engineering

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In order to make the moisture content monitoring more convenient and rapid during peanut drying process, the drying characteristics of peanut were investigated and a real‐time SVR moisture content monitoring model was established in this paper. The results showed that hot air temperature, initial moisture content, airflow rate and the layer height were the key factors on peanut drying, and the peanut variety showed little effect on drying. The SVR model exhibited a good performance with R2: 0.91, RMSE: 4.38, and bias: −7.5e‐3. Compared with the results of linear regression models and multilayer perceptron model, SVR model showed a better performance. In addition, the SVR model was validated by the drying data of other three varieties of peanuts. And the relative errors between the predicted values by SVR model and the measured values were within 20%, which suggested that SVR was a promising modeling algorithm for peanut drying. Practical application Peanut drying is essential for peanut production due to its high moisture content at harvest (about 30–50% on the wet basis), which makes it susceptible to mildew, or even produces aflatoxins. Peanut has a special physiological structure. In the drying process, the peanut kernels are gradually shrunk, which makes the air layer volume between the shell and the kernel gradually increases, thus hindering mass and heat transfer and leading to its different drying characteristics from other grain and oil crops. Furthermore, it is necessary to monitor the moisture content changes of peanut during deep bed drying to ensure the drying uniformity and prevent energy waste. Therefore, the drying characteristics of peanuts were studied and a moisture content prediction model for deep bed drying was established to assist the actual drying process.

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Peanut (Arachis hypogaea L.)

Mühlbauer¹,

Müller²

2020

Drying Atlas

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Simulation and Validation of Thin Layer Models for Peanut Drying

Cited by 18 publications

References 12 publications

Optimizing the drying parameters of a fixed bed with reversing ventilation for peanut using computer simulation

Optimizing the drying parameters of a fixed bed with reversing ventilation for peanut using computer simulation

A moisture content prediction model for deep bed peanut drying using support vector regression

Peanut (Arachis hypogaea L.)

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