Infrared convective drying of walnut with energy-exergy perspective

Dolgun, Gülşah Karaca; Aktaş, Mustafa; Dolgun, Ekin Can

doi:10.1016/j.jfoodeng.2021.110638

Cited by 21 publications

(19 citation statements)

References 33 publications

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“…However, the drying times observed in this study ranging from 300 to 480 min, were relatively short compared to those obtained in the study conducted by (Dolgun et al, 2021), who used infrared convective drying to dry walnuts with an energy‐exergy perspective lasting between 640 and 800 min. Similarly, (El‐Mesery et al, 2022) found that the drying time of garlic slices was between 540 and 720 min when employing a combined far‐infrared radiation dryer.…”

Section: Resultscontrasting

confidence: 54%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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Drying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. However, graphene may provide significant improvements during the drying process due to its ability to save energy and convert electro‐thermal energy effectively. In this study, random forest (RF), surface vector machine (SVM), artificial neural network (ANN), and k‐nearest neighbor (kNN) were the four machine‐learning algorithms employed to analyze the drying process of two types of corn (ZD88 and RP909) at varying temperatures of 35, 45, and 55°C in a graphene far‐infrared drying. The study found that the drying process of corn showed a decrease in moisture ratio during the falling rate period from 35 to 55°C. The moisture diffusivity increased from 35 to 55°C with the rise in the temperature, ranging from 2.74 to 4.36 × 10−8 m2/s for ZD88 and 2.05 to 3.07 × 10−8 m2/s for RP909. The ZD88 showed a heat efficiency of 81.62% at 55°C. The results of the machine learning algorithms revealed that 1–6 ANN was the best architecture. Normalized PUK was the best for SVM filters. A k = 3 and s‐fold = 5 were the best values for k‐NN and RF, respectively. Among the computed algorithms, the tested data for normalized Pearson universal kernel SVM filters proved to be the most effective in computing the process of drying corn. Overall, a graphene far‐infrared dryer showed improved heating efficiency and less energy consumption, providing a new concept for developing a drying process through machine learning algorithms strategies.Practical applicationsDrying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. This study provides significant improvements due to the ability of graphene irradiation heating plates to save energy and convert electro‐thermal energy effectively. The study achieved a heating efficiency of 81.62% at an infrared temperature of 55°C. Machine learning algorithms are intelligent models that minimize the limitations in describing the drying process of agricultural materials. The study found the normalized Pearson universal kernel was the most effective computation SVM filter. Therefore, using a graphene far‐infrared dryer showed a better drying alternative for industrial applications, as it has the potential to improve heating efficiency and consume less energy, providing a new concept for developing drying equipment through machine learning algorithms modeling strategies.

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Section: Resultscontrasting

confidence: 54%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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“…The low energy consumption of heat-pump-drying systems contributes to the increase in the products’ price competitiveness [ 19 , 20 ]. Infrared drying technology offers advantages such as a high energy efficiency, a short drying time, an even heating of materials, easy control of material temperature, high-quality final products, and low energy costs [ 21 ]. Freeze-drying leads to relatively high nutrient retention, size stabilization, color retention and a good rehydration ability, but has a high drying cost [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of the Periodic Drying Method on the Drying Time of Hazelnuts and Energy Utilization

Akgün,

Kontaş

2024

Foods

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Hazelnut is a shelled fruit that is stored by drying and used as a snack or in industry. Since the hazelnut drying process is energy-intensive, there is a need for drying methods that will reduce the energy cost without lengthening the drying time. In this study, the effects of periodic drying of hazelnuts’ energy recovery, oil, and protein content, as well as mass losses, were studied. Fresh Tombul hazelnuts (Corylus avellana L.) with a diameter of Ø 15–16 mm were dried in a tunnel dryer over 16 different periods by adjusting the drying time inside and waiting time outside the oven until the moisture content reached 6%. Drying experiments were carried out at 45 °C and three different air velocities. The increase in air velocity resulted in a reduction in the periodic drying time between 10% and 36%. The optimum drying in terms of drying time and energy utilization was realized at 0.5 m/s air velocity, with a 1.5 h working time and 0.5 h waiting time. During this period, drying time increased by 19% and energy utilization was 69%. For periodic drying, the increase in oven working time causes a decrease in energy utilization, while the increase in waiting time causes an increase in energy utilization and drying time. Periodic drying had no negative effect on hazelnut oil and protein content. Periodic drying is a suitable option for saving energy during hazelnut drying.

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“…Over recent decades, drying technology has been increasingly used in the walnut industry. Although different drying technologies such as infrared drying [5], microwave drying [6], vacuum drying [7], radiofrequency drying [7][8][9], intermittent oven drying [10,11], and combined drying [7] have been investigated in a laboratory for efficient drying, hot air drying (HAD) is still predominantly used in industrial production due to limitations in processing ability and running cost [12]. However, HAD usually has low drying efficiency, high energy consumption, and poor quality, which seriously restricts the development of the walnut industry [13,14].…”

Section: Introductionmentioning

confidence: 99%

Drying Kinetics and Mass Transfer Characteristics of Walnut under Hot Air Drying

Man,

Li,

Fan

et al. 2024

Agriculture

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This study was conducted to investigate the drying kinetics and internal and external mass transfer characteristics of walnuts for an understanding of the drying mechanism. The drying characteristics, mass transfer characteristics, and color of walnut during hot air drying (HAD) were investigated under different initial moisture content (IMC) (0.35, 0.39, and 0.43 g water/g wet mass) and drying temperatures (50, 60, 70, and 80 °C). The results indicated that the IMC and drying temperature both have significant effects on the drying process of walnut, showing the higher the IMC, the longer the preheating time, the smaller the effective moisture diffusivity (Deff) and mass transfer coefficient (hm), and the longer the drying time, but reverse results for drying temperature. The values of Deff and hm for walnut ranged from 4.94 × 10−10 to 1.44 × 10−9 m2/s and 1.24 × 10−7 to 3.90 × 10−7 m/s, respectively. The values of activation energy for moisture diffusion and mass transfer ranged from 21.56 to 23.35 kJ/mol and 28.92 to 33.43 kJ/mol, respectively. Multivariate linear prediction models were also established for estimating the Deff and hm as a function of the HAD process parameters. The drying temperature has a greater effect on the walnut kernel lightness than the IMC. The Verma et al model could be used to describe the HAD process of the walnut. The findings contribute to the understanding of moisture transfer mechanisms in walnuts and have practical value for the evaluation and improvement of drying systems.

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Infrared convective drying of walnut with energy-exergy perspective

Cited by 21 publications

References 33 publications

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

The Effect of the Periodic Drying Method on the Drying Time of Hazelnuts and Energy Utilization

Drying Kinetics and Mass Transfer Characteristics of Walnut under Hot Air Drying

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