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Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System

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  • Ahmet Beyzade Demirpolat

    (Support Services Department, Municipality of Elazig, 23200 Elazig, Turkey)

Abstract

In drying systems, the examination of the drying rate values of the food product in advance gives important information about the raw material to be dried. In this study, thin-layer drying behavior of apple slices in a convective solar dryer was investigated. The experiments were carried out at a drying air temperature of 46–63 °C and a drying air speed of 0.7–1.8 m/s. In order to determine the drying kinetics, the mass change of apple slices was recorded under all drying air conditions. The effects of drying air temperature and speed, drying speed of apple slices, dimensionless moisture content, were investigated. In a solar drying system, thermal efficiency, solar radiation and air velocity values were measured. The drying kinetics of 15-mm thick apple slices were examined for three days in the solar drying system. Using the decision tree algorithm, which is a machine learning algorithm, a predictive model was created for moisture rate in drying experiments and four linear equations were obtained. According to obtained equations, the collector in the drying system depends on the inlet–outlet temperature values, the drying room inlet–outlet temperature values, the drying room humidity values and air velocity values. Moisture rate data were applied to twelve different models and their performance was determined by root mean square error (RMSE) analysis. The mathematical model with the least error rate was (RMSE: 0.09) Midilli model. A comparison was made between these drying models in the literature and the model generated by the decision tree algorithm. According to the results of RMSE error analysis, it was shown that the model created with the decision tree algorithm predicted the moisture rate values with less error values RMSE: 0.03) than the Midilli model.

Suggested Citation

  • Ahmet Beyzade Demirpolat, 2019. "Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System," Energies, MDPI, vol. 12(18), pages 1-14, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3447-:d:264899
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    References listed on IDEAS

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    1. Yaovi Ouézou Azouma & Lynn Drigalski & Zdeněk Jegla & Marcus Reppich & Vojtěch Turek & Maximilian Weiß, 2019. "Indirect Convective Solar Drying Process of Pineapples as Part of Circular Economy Strategy," Energies, MDPI, vol. 12(15), pages 1-18, July.
    2. Atalay, Halil & Turhan Çoban, Mustafa & Kıncay, Olcay, 2017. "Modeling of the drying process of apple slices: Application with a solar dryer and the thermal energy storage system," Energy, Elsevier, vol. 134(C), pages 382-391.
    3. Yaldiz, Osman & Ertekin, Can & Uzun, H.Ibrahim, 2001. "Mathematical modeling of thin layer solar drying of sultana grapes," Energy, Elsevier, vol. 26(5), pages 457-465.
    4. Beigi, Mohsen & Torki-Harchegani, Mehdi & Tohidi, Mojtaba, 2017. "Experimental and ANN modeling investigations of energy traits for rough rice drying," Energy, Elsevier, vol. 141(C), pages 2196-2205.
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    Cited by:

    1. Md Imran H. Khan & C. P. Batuwatta-Gamage & M. A. Karim & YuanTong Gu, 2022. "Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling," Energies, MDPI, vol. 15(24), pages 1-27, December.
    2. Atalay, Halil & Yavaş, Nur & Turhan Çoban, M., 2022. "Sustainability and performance analysis of a solar and wind energy assisted hybrid dryer," Renewable Energy, Elsevier, vol. 187(C), pages 1173-1183.
    3. Ding Ding & Wenjing He & Chunlu Liu, 2021. "Mathematical Modeling and Optimization of Vanadium-Titanium Black Ceramic Solar Collectors," Energies, MDPI, vol. 14(3), pages 1-20, January.
    4. Kamil Neyfel Çerçi & Mehmet Daş, 2019. "Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems," Sustainability, MDPI, vol. 11(18), pages 1-16, September.

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