Comparison of the energy and exergy parameters in cantaloupe (Cucurbita maxima) drying using hot air

Zadhossein, Safoura; Abbaspour‐Gilandeh, Yousef; Kaveh, Mohammad; Nadimi, Mohammad; Paliwal, Jitendra

doi:10.1016/j.atech.2023.100198

Cited by 9 publications

(8 citation statements)

References 61 publications

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“…The lowest energy consumption for hot air and infrared dryers was calculated as 11.67 and 8.75 MJ/kg, respectively. By increasing air temperature, the SEC in both dryers (IR and hot air) decreased due to increase in thermal gradients and faster exit of moisture from saffron petals (Zadhossein et al, 2023). So that the temperature of 70°C compared to the temperature of 40°C for the hot air dryer reduced the SEC by 29.5%.…”

Section: Resultsmentioning

confidence: 99%

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

Sharifian

Gharkhloo

Yamchi

et al. 2023

J Food Process Engineering

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In this study, the effect of infrared (IR) and hot air dryers on the drying properties of saffron petals was investigated. Drying of saffron petals was done under three level temperature 40, 50, and 60 C and distance from IR lamps 10, 14, and 20 cm and the main physical and thermal properties were measured. The highest value effective moisture diffusion (D eff ) was evaluated for IR-dried samples at 60 C and 10 cm with 5.57 Â 10 À7 m 2 /s. The lowest value of color changes is related to the sample dried at 40 C at a distance of 20 cm with a color change of 11.99 in the IR dryer. Increasing the drying temperature reduced the drying time and energy consumption and increased the drying rate and D eff . Also, the results showed that Midilli model was selected as the best model for drying saffron petals with an IR and hot air dryer. Practical ApplicationsDue to the importance and sensitivity of saffron petals to the application of heat in the drying process, it is necessary to dry this product like other medicinal plants scientifically. Theoretical research, with the help of mathematical modeling and numerical simulation, provides a powerful and fast tool for analyzing the process of mass and heat transfer under natural drying conditions. Careful study of the drying kinetics of the product, evaluation of parameters affecting the physical quality properties, and the concomitant and reciprocal effects of these parameters is necessary. It can be used in the drying industry of medicinal plants.

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Section: Resultsmentioning

confidence: 99%

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

Sharifian

Gharkhloo

Yamchi

et al. 2023

J Food Process Engineering

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“…The drying process for various biomaterials was shown to be highly predictable and have an appropriate prediction of the capacity for ANNs. ANN and ANFIS models were used by (Zadhossein et al, 2023) to predict the MR, and the results had the highest R 2 prediction values at 0.9978 and 0.9940, respectively. However, (Okonkwo et al, 2022) observed that ANNs outperformed ANFIS with RMSE and correlation coefficient values of 0.0225 and 0.99786, respectively.…”

Section: Resultsmentioning

confidence: 99%

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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“…Thus, the drying process must be further researched to optimize grain quality preservation, energy consumption, and drying rate. In this regard, scientists are exploring theoretical and numerical models to optimize available drying systems (Gavi, 2020; Harchegani et al., 2012; Sarker et al., 2015, 2016; Zadhossein et al., 2023), introduce novel drying techniques (Das et al., 2009; Evin et al., 2008; Jafari & Zare, 2017; Jittanit et al., 2010; Soponronnarit et al., 2006), and/or develop fast, automated, and non‐destructive grain quality evaluation systems.…”

Section: Handler's Perspective On Grain Qualitymentioning

confidence: 99%

Enhancing traceability of wheat quality through the supply chain

Nadimi

Hawley²,

Liu

et al. 2023

Comp Rev Food Sci Food Safe

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With the growing global population, the need for food is expected to grow tremendously in the next few decades. One of the key tools to address such growing food demand is minimizing grain losses and optimizing food processing operations. Hence, several research studies are underway to reduce grain losses/degradation at the farm (upon harvest) and later during the milling and baking processes. However, less attention has been paid to changes in grain quality between harvest and milling. This paper aims to address this knowledge gap and discusses possible strategies for preserving grain quality (for Canadian wheat in particular) during unit operations at primary, process, or terminal elevators. To this end, the importance of wheat flour quality metrics is briefly described, followed by a discussion on the effect of grain properties on such quality parameters. This work also explores how drying, storage, blending, and cleaning, as some of the common post-harvest unit operations, could affect grain's end-product quality. Finally, an overview of the available techniques for grain quality monitoring is provided, followed by a discussion on existing gaps and potential solutions for quality traceability throughout the wheat supply chain.

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Comparison of the energy and exergy parameters in cantaloupe (Cucurbita maxima) drying using hot air

Cited by 9 publications

References 61 publications

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

Infrared and hot drying of saffron petal (Crocus sativus L.): Effect on drying, energy, color, and rehydration

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

Enhancing traceability of wheat quality through the supply chain

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