Experimental and Modeling Study of Peanut Drying in a Solar Dryer with a Novel Type of a Drying Chamber

Hürdoğan, Ertaç; Çerçi, Kamil Neyfel; Saydam, Doğan Burak; Özalp, Coşkun

doi:10.1080/15567036.2021.1974126

Cited by 13 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The drying chamber with an inclined structure was used to obtain homogeneous temperature distribution and to increase the drying performance. More detailed information about the drying channel and the system can be found in the other studies of the authors [22][23][24].…”

Section: Methodsmentioning

confidence: 99%

Drying of mushroom slices in a new type solar drying system and under open sun: Experimental and mathematical investigation

Çerçi

Saydam

Hürdoğan

2022

European Mechanical Science

Self Cite

View full text Add to dashboard Cite

Drying is among the beneficial food preservation strategies and this method ensures food products last before they reach consumers. The most used drying method is direct drying under the sun. However, in this method, the negative effects of the external environment damage food products. Recently, solar drying systems have been the main subject of much research as they have been protecting food from the negative effects of the external environment. In this study, a solar drying system (SD), which have a drying chamber with different structure, was used for drying mushroom. At the same time, mushroom slices were dried under open sun (OSD) for observing the performance of drying system. Drying rate (DR) and moisture ratio (MR) values were determined from the experiments. In addition, the MR values obtained from the experiments were estimated by 6 different mathematical models and 6 different machine learning algorithms. According to the results of the experiments, the drying time of the mushroom slices using SD was approximately 12.4 hours less than the drying time under open sun. The best convergence in the results gathered from the mathematical models is Sripinyowanich and Noomhorn and Hii et al. models for SD and OSD, respectively. The best estimation for MR values was realized by the Multilayer Perception algorithm for both drying methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Drying of mushroom slices in a new type solar drying system and under open sun: Experimental and mathematical investigation

Çerçi

Saydam

Hürdoğan

2022

European Mechanical Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…It was found that the ANN configuration containing a total of 170 neurons and a resilient backpropagation (BP) training algorithm with a hyperbolic tangent sigmoid transfer function showed the best prediction-based outcomes for the shelled corn drying system (Momenzadeh et al, 2011). Hürdoğan et al (2021) developed ML-based models using various supervised learning algorithms, including DTs, linear regression (MLR), and the SVM, for predicting the drying kinetics of peanut drying in a solar dryer. For DT models, reduced error pruning tree (sometimes referred to as RepTree), random tree, and M5P tree methods were used to predict the drying rate and moisture ratio during peanut drying.…”

Section: In Food Dryingmentioning

confidence: 99%

“…Hürdoğan et al. (2021) developed ML‐based models using various supervised learning algorithms, including DTs, linear regression (MLR), and the SVM, for predicting the drying kinetics of peanut drying in a solar dryer. For DT models, reduced error pruning tree (sometimes referred to as RepTree), random tree, and M5P tree methods were used to predict the drying rate and moisture ratio during peanut drying.…”

Section: In Food Processing Applicationsmentioning

confidence: 99%

Machine learning‐based modeling in food processing applications: State of the art

Khan

Sablani

Nayak

et al. 2022

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better‐quality products. The development of a machine learning (ML)‐based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better‐quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML‐based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step‐by‐step procedure to develop an ML‐based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML‐based techniques to hybrid food processing operations. The potential of physics‐informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML‐based technology for food processing applications.

show abstract

“…From the obtained results, they indicated that heat transfer coefficient variations along the surface area of the model. Hürdoğan et al (2022) investigated the experimental performance of a solar dryer, employing an innovative drying chamber design to enhance drying efficiency and promote uniform drying. According to findings, the temperature distribution on the products within the chamber was more uniform in the newly designed drying chamber compared to the conventional one.…”

Section: Introductionmentioning

confidence: 99%

Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Kilic,

Sahin,

Hassan

et al. 2024

J Food Process Engineering

View full text Add to dashboard Cite

A significant part of the world's population still has problems in accessing food. The growing world population will exacerbate this issue in the future. Innovative studies conducted in this field play a crucial role in addressing the issue of drying and storage of foods. Atmospheric drying methods, such as rotary, tunnel, conveyor, cabinet, tower, and kiln dryers, offer advantages in relation to high production capacities, cost‐effective initial setup, and economical operating conditions. However, concurrently, the weaknesses of these methods arise from factors such as drying duration, uneven moisture content, and space requirements. The solar dryer method is especially effective in dehydrating agricultural products, offering an energy‐saving advantage compared to other methods. However, it is important to note that this approach, which involves exposing crops to direct sunlight, comes with several drawbacks affecting both food quality and health. In cases where the quality of highly valued foodstuffs is crucial, subatmospheric drying methods like vacuum, freeze, and microwave freeze are typically preferred. However, the primary drawback of this approach lies in its high operating costs, particularly in terms of energy consumption. Artificial neural networks (ANNs) can be used for predictive modeling, helping to forecast drying behavior and optimize process parameters in food drying applications especially nonlinear connections among variables. ANNs are adept at managing nonlinearities, offering a more precise depiction of the intricate interactions within food drying systems. This review examines articles from the last 5 years in the literature, synthesizing research conducted in food drying. The findings indicate a predominant interest among researchers in methodologies with lower environmental impact, prompting increased attention to studies addressing this aspect. There is a notable emphasis on the frequent exploration of energy‐efficient systems. The ongoing research focuses on the development of methods utilizing ultrasonic, infrared radiation, and electrohydrodynamic techniques to achieve more effective, shorter‐duration, energy‐efficient drying processes with enhanced control over the final product.

show abstract

Experimental and Modeling Study of Peanut Drying in a Solar Dryer with a Novel Type of a Drying Chamber

Cited by 13 publications

References 47 publications

Drying of mushroom slices in a new type solar drying system and under open sun: Experimental and mathematical investigation

Drying of mushroom slices in a new type solar drying system and under open sun: Experimental and mathematical investigation

Machine learning‐based modeling in food processing applications: State of the art

Preservation of fruits through drying—A comprehensive review of experiments and modeling approaches

Contact Info

Product

Resources

About