Energy savings in convective drying of various materials using variable temperature conditions

Doder, Đorđije; Đaković, Damir

doi:10.1002/ep.13277

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…These values correspond to a variation of 9.8 × 10 −11 to 17.6 × 10 −11 m 2 /s when the temperature is increased from 40 to 80°C. According to Doder and Đaković (2020) this range of effective diffusivity is not suitable to save energy when drying materials using intermittent drying or temperature variations.…”

Section: Resultsmentioning

confidence: 99%

A new approach to the traditional drying models for the thin‐layer drying kinetics of chickpeas

Cavalcanti‐Mata

Duarte

Lira

et al. 2020

J Food Process Engineering

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The drying process is an essential step in the postharvest of grains. The objective of this research was to study the drying kinetics of chickpeas (Cicer arietinum L.), by introducing a new approach to simplify the Fick equation. Drying temperatures of 40, 50, 60, 70, and 80 C were used in a hot air convective dryer to dry thin-layers of chickpeas. The mathematical models (Fick, modified Henderson & Pabis, modi

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Section: Resultsmentioning

confidence: 99%

A new approach to the traditional drying models for the thin‐layer drying kinetics of chickpeas

Cavalcanti‐Mata

Duarte

Lira

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Drying processes can be energy-intensive, and improving energy efficiency is a key challenge in the field. Although energy efficiency can be improved in different ways, such as by changing the nature of the drying process [72], the use of machine learning algorithms can significantly improve energy efficiency by reducing the need for experiments [73].…”

Section: Energy Efficiency Issues Of Dryingmentioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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Comparison of different solar dryers with and without heat storage for crop drying: A review

S.,

Shankar,

Ezhilarasan

et al. 2024

Env Prog and Sustain Energy

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Solar drying systems have proven to be cost‐effective and environmentally friendly for drying various products. An extensive investigation of solar dryers with different designs, applications, and operating principles is conducted. Compared to direct drying, solar drying improves product quality by lowering moisture content without compromising the original product's qualities. Solar dryers are of two types: passive solar dryers and active solar dryers. Passive‐mode solar dryers circulate air through natural convection, whereas active ones circulate air through forced convection. Crop‐affected design characteristics include crop sensitivity, cost and time available to dry the crop, crop moisture content, the level of drying required, and so on. A wide range of research has been carried out on the thermal modeling of the dryer, using different designs. This article also discussed and compared the drying performance of three basic solar dryers: direct solar dryer, dryer with PVT air collection, and phase change materials (PCM). Even though PCM dryers are more expensive and uncommon, their ability to speed up drying at night makes them impactful.

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Energy savings in convective drying of various materials using variable temperature conditions

Cited by 6 publications

References 20 publications

A new approach to the traditional drying models for the thin‐layer drying kinetics of chickpeas

A new approach to the traditional drying models for the thin‐layer drying kinetics of chickpeas

Review of Energy-Related Machine Learning Applications in Drying Processes

Comparison of different solar dryers with and without heat storage for crop drying: A review

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