Optimization study of soybean intermittent drying in fixed-bed drying technology

Defendi, Rafael Oliveira; Paraíso, Paulo Roberto; Jorge, Luíz Mário de Matos

doi:10.1080/07373937.2016.1162171

Cited by 22 publications

(21 citation statements)

References 39 publications

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“…However, if process conditions are not controlled effectively, the drying can damage the material and energy consumption can reach unfeasible levels. In this regard, several authors (Benali, ; Defendi, Nicolin, Paraíso, & Jorge, ; Defendi, Paraíso, & Jorge, , ; Fregolente, Santos, & Jorge, ; Golmohammadi, Rajabi‐Hamane, & Hashemi, ; Itaya, Mori, & Hasatani, ; Meisami‐asl, Rafiee, Keyhani, & Tabatabaeefar, 2010; Shei & Chen, ; Vega‐Gálvez et al, ) have studied the drying process of agricultural products in order to determine the effects that control the process, adjust, and propose mathematical models of drying kinetics or optimize the process to minimize energy consumption or minimize the loss of material quality.…”

Section: Introductionmentioning

confidence: 99%

“…Most agricultural products do not undergo the drying stage at constant rates, so that the drying rate decreases from beginning to end in the process (Benali, ; Defendi, Nicolin, et al, ; Defendi, Paraíso, et al, , ; Meisami‐asl et al, ; Oliveira & Rocha, ; Vega‐Gálvez et al, ). Thus, internal diffusive effects of the material can affect the process more intensely and they will increasingly affect the process up to the equilibrium stage, when the net rate of drying is zero (Defendi, Nicolin, et al, ; Defendi, Paraíso, et al, , ). Due to this behavior, it is appropriate to work with intermittent drying, as observed in some studies (Golmohammadi et al, ; Itaya et al, ), since it is possible to lower energy consumption and better preserve the quality of material, compared with drying with air supply in steady state conditions, since the inner diffusive effect of the material is what controls the process.…”

Section: Introductionmentioning

confidence: 99%

“…Another variable that has a strong influence on the drying rates is the air temperature (Benali, ; Defendi, Nicolin, et al, ; Defendi, Paraíso, et al, , ; Meisami‐asl et al, ; Oliveira & Rocha, ). Some authors (Benali, ; Defendi, Paraíso, et al, ; Vega‐Gálvez et al, ) have observed that air velocity has less influence than air temperature, while others found that air velocity did not affect thin layer drying (Defendi, Nicolin, et al, ; Yaldýz & Ertekýn, ). For the same type of soybean, it was observed that air velocity affected thick layer but had no effect on drying rates on thin layer (Defendi, Nicolin, et al, ; Defendi, Paraíso, et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Some authors (Benali, ; Defendi, Paraíso, et al, ; Vega‐Gálvez et al, ) have observed that air velocity has less influence than air temperature, while others found that air velocity did not affect thin layer drying (Defendi, Nicolin, et al, ; Yaldýz & Ertekýn, ). For the same type of soybean, it was observed that air velocity affected thick layer but had no effect on drying rates on thin layer (Defendi, Nicolin, et al, ; Defendi, Paraíso, et al, ). It was also observed that the drying rates can increase with increasing surface area of the product on contact with the drying air (Meisami‐asl et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Because of the many variables that can affect the process, such as temperature, humidity, and velocity of the drying gas, and because drying is a transient process that involves both mass and energy transfer between the solid material and gas, generalized mathematical models can be complex, with several parameters to be adjusted. Thus, the models that have been adjusted by some authors (Defendi, Nicolin, et al, 2016;Defendi, Paraíso, et al, 2016a, 2016bElGamal, Ronsse, ElMasry, & Pieters, 2015;Golmohammadi et al, 2012;Isquierdo et al, 2013;Itaya et al, 1999;Lawrence, Atungulu, & Siebenmorgen, 2015;Lima & Nebra, 2001;Meisami-asl et al, 2010) may differ as to the applicability of conditions of the considerations and simplifications adopted by these authors.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Mathematical modeling of soybean drying by a fractional‐order kinetic model

Nicolin

Defendi

Rossoni

et al. 2017

J Food Process Engineering

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In this article, a mathematical model based on the generalization of first‐order kinetic model, to model soybean drying kinetic, is proposed by taking into account that the humidity variation rate is given by a derivative of arbitrary order. The Bootstrap technique was used to study the minimum required number of terms of the analytic solution to fit the parameters with stable variability. Results were highly successful for the estimated moisture curves. These results were compared with the first‐order kinetic model and with the classic Page model. Series solution minimum number of terms varied both with respect to the model parameters and temperature. Practical applications The modeling procedure presented in this article presents the use of fractional calculus as a potential tool to generalize mathematical models based on differential equations. The proposed model proved to be statistically better than its version which was based on conventional calculus. Thus, the model proposed is suitable for modeling kinetic processes in general and can be used to project drying equipment. Due to the fact that drying of food could present nonexponential behavior, the fractional model proposed would be an adequate option to capture the characteristics of the kinetic curve which the conventional calculus cannot. In addition, this article presents a statistical approach to evaluate the correct number of terms to be used in an equation based on an infinite series in order to result the least variability possible. Such approach provides more reliable results when the model is applied to experimental data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mathematical modeling of soybean drying by a fractional‐order kinetic model

Nicolin

Defendi

Rossoni

et al. 2017

J Food Process Engineering

Self Cite

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Mathematical modelling, energy consumption, and quality evaluation of wheat seeds subjected to intermittent drying

Aranha,

Defendi,

Suzuki

et al. 2024

Can J Chem Eng

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This work aims to evaluate the kinetic profile of intermittent drying of wheat seeds using traditional models from the literature and the fractional calculus technique. Furthermore, it aims to verify the application of the intermittent drying process on the amount of antioxidant compounds, protein, and lipid content of the grain, in addition to energy consumption to obtain the desired final moisture content of the wheat. It was verified that the prediction of drying kinetics by Page and fraction order models were similar (modelling efficiency varying between the range of 0.917–0.995, varying the drying condition and wheat cultivar). Regarding antioxidant compounds for the three wheat cultivars, it can be seen that the higher fraction of ethanol (74.0% and 90.36%) used for extraction had greater process efficiency. Regarding the protein content in the three wheat cultivars, lower drying temperatures and intermittency periods result in lower quality losses of material (12.3% for BRS‐Atobá wheat, 9.89% for BRS‐Jacana wheat, and 18.71% for BRS‐Sanhaço wheat). In terms of lipids, it was found that the influence of temperature was greater on the lipid content than on the protein content of the material (for the best drying condition, there were percentage decreases for the best condition of 36.16% for the BRS‐Atobá wheat, 34.9% for BRS‐Jacana, and 52.2% for BRS‐Sanhaço). Regarding energy consumption during the drying process, it can be seen that the conditions used for intermittency and drying time, in addition to the sample conditions, directly impact energy consumption.

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Heat consumption and modeling of corn seeds intermittent drying

Raimundini Aranha,

Lopes Ferrari,

Paschoal

et al. 2023

JSFA Reports

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BACKGROUNDCorn seed is produced on a large scale, and as production is carried out seasonally, the storage is needed to supply uninterrupted demand. To carry out the storage, as the material from the harvest has a high initial moisture content, it is necessary to carry out the drying process. In order to reduce energy consumption, an alternative is intermittent drying. The purpose of this article is to fit mathematical model of intermittent drying by distributed parameters, based on experimental data of temperature and moisture content of corn seeds submitted to intermittent drying, to determine the diffusivity value and to assess heat consumption.RESULTSFor samples dried to the final moisture content of 16.0 % in dry basis (d.b.), the condition of intermittent drying at a temperature of 40 °C and 10 minutes of tempering period led to the lowest heat consumption, in comparison to conventional drying and to intermittent drying under higher temperatures. In addition, to obtain a final moisture content of 14.0 % (d.b.), the conventional drying condition at 55 °C resulted in the lowest heat consumption, compared to the other drying conditions. The distributed parameter model can predict the experimental data with a global deviation lower than 10.0 %CONCLUSIONAnalyzing the moisture distribution profiles for the corn seeds, it was verified that there was a difference in the moisture content along the position inside the material, being necessary to evaluate the influence of moisture distribution along the position inside the material in kinetic drying models.This article is protected by copyright. All rights reserved.

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Optimization study of soybean intermittent drying in fixed-bed drying technology

Cited by 22 publications

References 39 publications

Mathematical modeling of soybean drying by a fractional‐order kinetic model

Mathematical modeling of soybean drying by a fractional‐order kinetic model

Mathematical modelling, energy consumption, and quality evaluation of wheat seeds subjected to intermittent drying

Heat consumption and modeling of corn seeds intermittent drying

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