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 (hc) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine hc. 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 (hc), 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 hc 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 hc values in GDNC (RMSE = 0.0815).
Highlights
The short wavelengths emitted by the sun’s radiant energy are absorbed through permeable air and reflect on the earth’s surface as infrared radiation
One of the most widely used classification methods for thermal modelling of greenhouse drying systems is the air circulation method in the drying chamber [11,16]. These systems are classified in two different ways, prior to air circulation: The first is natural convection greenhouse dryers, in which solar radiation passes through the transparent cover to heat the product and increase its temperature
The results proved that decision trees interpret predictions for complex data in medium and long periods of agricultural experience [37]
Summary
The short wavelengths emitted by the sun’s radiant energy are absorbed through permeable air and reflect on the earth’s surface as infrared radiation. One of the most widely used classification methods for thermal modelling of greenhouse drying systems is the air circulation method in the drying chamber [11,16] These systems are classified in two different ways, prior to air circulation: The first is natural convection greenhouse dryers, in which solar radiation passes through the transparent cover to heat the product and increase its temperature. The convective heat transfer coefficient value (hc) has a significant effect on the drying rate, due to the temperature difference between the product and air. The use of MLP and DT models to estimate convective heat transfer coefficients obtained experimentally from food drying processes has not been found in open literature. Our study, which is based on the modeling of a convective heat transfer coefficient with DT and MLP in drying systems, is expected to be a case study for many researchers
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