Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Kamil Neyfel Çerçi and Mehmet Daş
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Kamil Neyfel Çerçi: Mechanical Engineering Department, Faculty of Engineering, Osmaniye Korkut Ata University, Merkez 80000, Turkey
Mehmet Daş: Vocation High School of Ilic Dursun Yildirim, Erzincan Binali Yildirim University, Ilic, Erzincan 24700, Turkey

Sustainability, 2019, vol. 11, issue 18, 1-16

Abstract: As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient ( h c ) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine h c . However, as it is difficult to manually process and analyze large amounts of data and different formulations, machine learning algorithms are preferred. In this study, natural and forced convective solar greenhouse type dryers were designed. In a solar greenhouse type dryer, grape is dried in natural (GDNC) and forced convection (GDFC). For convective heat transfer coefficient ( h c ), predictive models were created using a multilayer perceptron (MLP)—which has many uses in drying applications, as mentioned in the literature—and decision tree (DT), which has not been used before in food drying applications. The machine learning algorithms and results of the estimated models are compared in this study. Error analyses were performed to determine the accuracy rates of the obtained models. As a result, the h c value of the dried grape product in a natural convective solar greenhouse type dryer was 11.3% higher than that of the forced type. The DT algorithm was found to be a more successful model than the MLP algorithm in estimating hc values in HDFC according to Root Mean Square Error. (RMSE = 0.0903). On the contrary, the MLP algorithm was more successful than the DT algorithm in estimating h c values in GDNC (RMSE = 0.0815).

Keywords: solar greenhouse dryer; heat transfer coefficient; machine learning algorithms; decision tree; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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