Havalı Güneş Kollektör Destekli Sera Gıda Kurutucu Sisteminin Performansının İncelenmesi

Year 2024, Volume: 2 Issue: 2, 77 - 84, 27.09.2024

Ebru Akpınar , Mehmet Daş

Abstract

Kurutma katı maddelerden ısıl yöntemlerle su veya uçucu maddelerin giderilmesi işlemini tanımlamaktadır. Güneş enerjisi ile tarım ürünlerini kurutma, en eski gıda saklama yöntemlerinden birisi olarak bilinmektedir. Güneşte kurutmada çevresel faktörler nedeniyle gıdanın kalitesi ciddi olarak azalmaktadır. Bu nedenle kurutma işleminin özel amaçlı yapay kurutucular ile yapılması hem kuruma süresini kısaltmakta hem de uzun raf ömrüne sahip daha kaliteli ve temiz ürün elde edilmesini sağlamaktadır. Bu çalışmada gıda kurutma kalitesini ve performansını arttırmak için havalı güneş kolektör (HGK) destekli sera tipi bir kurutucu tasarlanmıştır. Çalışmada Elazığ ili iklim şartlarında kurutma deneylere gerçekleştirilmiştir. Kurutma deneyleri sonrası kurutulacak ürünün ısı ve kütle transferi analizleri yapılmıştır. Deneylerde nem içeriği, nem oranı, konvektif ısı transfer katsayısı parametreleri hesaplanmıştır. Deneyler süresince, sera ve HGK giriş ve çıkış sıcaklıkları, güneş ışınımı ve ürün ağırlık değerleri 15 dakikalık periyotlarla ölçülmüştür. HGK desteği ile sera kurutucunun ürün kurutma süresi % 24 oranında azalmıştır. Böylelikle daha hızlı bir kurutma süreci elde edilmiştir. Ayrıca kurutma işlemlerinde önemli bir parametre olan konvektif ısı transfer katsayısı hesaplanmış ve bu parametre için makine öğrenmesi (MÖ) algoritmaları ile tahminsel modeller elde edilmiştir. Bu çalışmanın amacı, sera tipi gıda kurutucuların performansını arttırmak için havalı güneş kollektörü kullanmak ve konventif ısı transferi için MÖ algoritmaları kullanılarak faydalı modellerin üretilmesidir. Kısaca hem yapay zekâ hem de deneysel uygulamaların yapılacağı termodinamik bir sistem elde edilmiştir. Makine öğrenmesi algoritmaları olarak yapay sinir ağı (YSA) ve karar ağacı (KA) algoritmaları seçilmiştir. MÖ algoritmaları ile elde edilen model sonuçları ile deneysel sonuçlar karşılaştırılmıştır. Deneysel sonuçları ile YSA sonuçları arasındaki hata %1 iken, KA sonuçları arasındaki hata %7 dir.

Keywords

Sera kurutucu, güneş kollektörü, konvektif ısı transfer katsayısı, makine öğrenmesi

Investigation of Performance of Air Solar Collector Assisted Greenhouse Food Dryer System

Abstract

Drying refers to the process of removing water or volatile substances from solids by thermal methods. Drying agricultural products with solar energy is known as one of the oldest food storage methods. In solar drying, the quality of food is seriously reduced due to environmental factors. For this reason, drying with special purpose artificial dryers shortens the drying time and provides a better quality and cleaner product with a long shelf life. In this study, an air solar collector (ASC) supported greenhouse type dryer was designed to improve the quality and performance of food drying. In the study, drying experiments were carried out in the climatic conditions of Elazığ province. After the drying experiments, heat and mass transfer analysis of the product to be dried were carried out. Moisture content, moisture ratio, convective heat transfer coefficient parameters were calculated in the experiments. During the experiments, greenhouse and HGK inlet and outlet temperatures, solar radiation and product weight values were measured at 15-minute intervals. With the support of the HGK, the product drying time of the greenhouse dryer was reduced by 24%. Thus, a faster drying process was achieved. In addition, convective heat transfer coefficient, which is an important parameter in drying processes, was calculated and predictive models were obtained with machine learning (ML) algorithms for this parameter. The aim of this study is to use air solar collectors to improve the performance of greenhouse type food dryers and to produce useful models for convective heat transfer using ML algorithms. In short, a thermodynamic system in which both artificial intelligence and experimental applications will be performed has been obtained. Artificial neural network (ANN) and decision tree (DTA) algorithms were selected as machine learning algorithms. Model results obtained with ML algorithms are compared with experimental results. The error rate between the experimental results and ANN results is 1%, while the error rate between KA results is 7%.

Keywords

Greenhouse dryer, solar collector, convective heat transfer coefficient, machine learning

References

• Abdelkader, T. K., Sayed, H. A., Refai, M., Ali, M. M., Zhang, Y., Wan, Q., Abdelhamid, M. A. (2024). Machine learning, mathematical modeling and 4E (energy, exergy, environmental, and economic) analysis of an indirect solar dryer for drying sweet potato. Renewable Energy, 227, 120535.
• Akpinar, E. K. & Koçyiğit, F. (2010). Energy and exergy analysis of a new flat-plate solar air heater having different obstacles on absorber plates. Applied Energy, 87(11), 3438–3450.
• Akpinar, E. K. & Toraman, S. (2016). Determination of drying kinetics and convective heat transfer coefficients of ginger slices. Heat and Mass Transfer, 52, 2271–2281.
• Alic, E., Das, M., & Kaska, O. (2019). Heat flux estimation at pool boiling processes with computational intelligence methods. Processes, 7(5), 293.
• Anwar, S. I., & Tiwari, G. N. (2001). Evaluation of convective heat transfer coefficient in crop drying under open sun drying conditions. Energy Conversion and Management, 42(5), 627-637.
• Anwar, S. I., & Tiwari, G. N. (2001). Convective heat transfer coefficient of crops in forced convection drying–an experimental study. Energy Conversion and Management, 42(14), 1687-1698.
• Belessiotis, V., & Delyannis, E. (2011). Solar drying. Solar Energy, 85(8), 1665-1691.
• Çerçi, K. N., & Daş, M. (2019). Modeling of heat transfer coefficient in solar greenhouse type drying systems. Sustainability, 11(18), 5127.
• Chauhan, P. S., Kumar, A., Nuntadusit, C., & Banout, J. (2018). Thermal modeling and drying kinetics of bitter gourd flakes drying in modified greenhouse dryer. Renewable Energy, 118, 799-813.
• Daliran, A., Taki, M., Marzban, A., Rahnama, M., & Farhadi, R. (2023). Experimental evaluation and modeling the mass and temperature of dried mint in greenhouse solar dryer; Application of machine learning method. Case Studies in Thermal Engineering, 47, 103048.
• Das, M., & Akpinar, E. K. (2018). Investigation of pear drying performance by different methods and regression of convective heat transfer coefficient with support vector machine. Applied Sciences, 8(2), 215.
• Daş, M., Alıç, E., & Akpinar, E. K. (2021). Numerical and experimental analysis of heat and mass transfer in the drying process of the solar drying system. Engineering Science and Technology, an International Journal, 24(1), 236-246.
• de Jesús Rubio, J., Garcia, E., Ochoa, G., Elias, I., Cruz, D. R., Balcazar, R., ... & Novoa, J. F. (2019). Unscented Kalman filter for learning of a solar dryer and a greenhouse. Journal of Intelligent & Fuzzy Systems, 37(5), 6731-6741.
• Ghritlahre, H. K., Chandrakar, P., & Ahmad, A. (2020). Application of ANN model to predict the performance of solar air heater using relevant input parameters. Sustainable Energy Technologies and Assessments, 40, 100764.
• Jain, D., & Tiwari, G. N. (2004). Effect of greenhouse on crop drying under natural and forced convection I: Evaluation of convective mass transfer coefficient. Energy Conversion and Management, 45(5), 765-783.
• Janjai, S., Tohsing, K., Lamlert, N., Mundpookhier, T., Chanalert, W., & Bala, B. K. (2018). Experimental performance and artificial neural network modeling of solar drying of litchi in the parabolic greenhouse dryer. Journal of Renewable Energy and Smart Grid Technology, 13(1), 1-12.
• Khanlari, A., Sözen, A., Afshari, F., Şirin, C., Tuncer, A. D., & Gungor, A. (2020). Drying municipal sewage sludge with v-groove triple-pass and quadruple-pass solar air heaters along with testing of a solar absorber drying chamber. Science of The Total Environment, 709, 136198.
• Kumar, A., & Tiwari, G. N. (2007). Effect of mass on convective mass transfer coefficient during open sun and greenhouse drying of onion flakes. Journal of Food Engineering, 79(4), 1337-1350.
• Kushwah, A., Gaur, M. K., Kumar, A., & Sıngh, P. (2022). Application of ANN and prediction of drying behavior of mushroom drying in side hybrid greenhouse solar dryer: An experimental validation. Journal of Thermal Engineering, 8(2), 221-234.
• Rasooli Sharabiani, V., Kaveh, M., Abdi, R., Szymanek, M., & Tanaś, W. (2021). Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling. Scientific Reports, 11(1), 9155.
• Selimefendigil, F., Şirin, C., & Öztop, H. F. (2022). Improving the performance of an active greenhouse dryer by integrating a solar absorber north wall coated with graphene nanoplatelet-embedded black paint. Solar Energy, 231, 140-148.
• Zadhossein, S., Abbaspour‐Gilandeh, Y., Kaveh, M., Kalantari, D., & Khalife, E. (2022). Comparison of two artificial intelligence methods (ANNs and ANFIS) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer. Journal of Food Processing and Preservation, 46(10), e16836.