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).

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In this study, the dehydration behavior of zucchini using solar assisted drying system was examined according to 22 thin layer drying models available in literature. The correlation coefficient (R2), chi-square (χ2) and root mean square error (RMSE) values were calculated to check the suitability of models by non-linear regression analysis. It was found that Cubic and Modified Midilli-1 models were the most suitable equations and their R2 values were calculated as 0.99963. χ2 and RMSE values of related mathematical expressions were 1.89343×10‒5, 1.91692×10‒5 and 0.01685×10‒3, 0.01721×10‒3 respectively. In addition, heat transfer, mass transfer and diffusion coefficients, which were important parameters in design of drying systems were also determined as 5.18124 W/m2°C, 1.57129×10‒7 m/s and 2.335718×10‒9 m2/s respectively.

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