Infrared radiation drying of Moringa oleifera grains for use in water treatment

Nascimento, Vânia R. G.; Biagi, J. D.; Oliveira, Rafael Augustus de; Arantes, Camila Clementina; Rossi, Luiz Alexandre Solano

doi:10.1590/1807-1929/agriambi.v23n10p768-775

Cited by 3 publications

(2 citation statements)

References 17 publications

(23 reference statements)

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“…For example, (Jeevarathinam et al, 2021) employed infrared-assisted hot air drying to dry turmeric slices with air temperatures ranging from 50 to 70 C. This method consumed between 2.25 and 6.35 kWh of energy. Similarly, infrared radiation was used to dry Moringa oleifera grains for water treatment purposes, with IR temperatures ranging from 30 to 60 C, consuming 2.19-5.28 kWh of energy (Nascimento et al, 2019). However, recent studies have found that the air absorbs a significant proportion of energy during the drying process, reducing thermal efficiency.…”

Section: Total Energy Analysismentioning

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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Section: Total Energy Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…This method is simple and cost-effective, but it is marred by numerous drawbacks. These include dependence on weather conditions, prolonged drying times, and susceptibility to contamination from dust, insects, and dirt which ultimately lead to suboptimal, low-value end products [7,13,14]. Infrared (IR) drying, in contrast, is considered a more efficient method for enhancing the drying rate of agricultural products, using less energy than traditional drying methods [15].…”

Section: Introductionmentioning

confidence: 99%

Efficiency and Energy Consumption Analysis of Infrared-Assisted Drying of Oyster Mushrooms

Nurmawati,

Hadiyanto,

Cahyadi

et al. 2023

IJHT

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The potential of infrared drying to enhance energy efficiency and condense drying periods has been increasingly recognized. The present study is centered on the application of infrared lamps to extend the shelf life of oyster mushrooms, a widely consumed species. The drying performance and energy consumption of these mushrooms were evaluated using a batch method, highlighting the influence of varying power levels (432 W, 504 W, and 624 W), drying air velocities (1.2 m.s -1 and 1.5 m.s -1 ), and mushroom weight (500 g and 750 g) on the drying process. Parameters including changes in water content, specific energy consumption (SEC; kWh. kg of water -1 ), and drying energy efficiency (%) were meticulously observed. It was found that drying energy efficiency varied between 40.01 and 53.95%, while the SEC ranged from 2.81 to 3.58 kWh. kg of water -1 . The combination of 624 W power, 1.5 m.s -1 of drying air velocity, and a mushroom weight of 750 g yielded the highest drying efficiency (53.95%) and the lowest SEC (2.81 kWh. kg of water -1 ). Furthermore, the shortest drying time was observed when the conditions were adjusted to the highest power level (624 W), fastest air velocity (1.5 m.s -1 ), and lowest sample weight (500 g). This study underscores the promise of infrared drying in optimizing the drying process of oyster mushrooms, with implications for broader applications in food preservation.

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Influence of drying methods on health indicators of brewers spent grain for potential upcycling into food products

Thai¹,

Avena‐Bustillos²,

Alves³

et al. 2022

Applied Food Research

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Infrared radiation drying of Moringa oleifera grains for use in water treatment

Cited by 3 publications

References 17 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

Efficiency and Energy Consumption Analysis of Infrared-Assisted Drying of Oyster Mushrooms

Influence of drying methods on health indicators of brewers spent grain for potential upcycling into food products

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