Mushroom Drying in Air Heated Solar Collector Drying System and Modeling of Drying Performance with Artificial Neural Network

Yıl 2018, Cilt: 11 Sayı: 1, 23 - 30, 24.04.2018

Mehmet Daş , Ebru Kavak Akpınar

Öz

Bu çalışmada, mantarın kuruma özelliğini tespit etmek üzere hava ısıtmalı güneş kollektörlü (HIGK) kurutucu tasarlanmıştır. Deneylerde ince dilimlenmiş mantar örnekleri kullanılmıştır. Kollektör giriş ve çıkış havaları sıcaklıkları, kurutma odası giriş ve çıkış havaları sıcaklıkları, çevre sıcaklığı, ışınım, hava hızı ve kuruma hızı; kuruma özelliğini etkileyen parametreler olarak düşünülmüştür. Elde edilen sonuçlar, kurutma zamanının fonksiyonu olarak sunulmuştur. Deneylerden elde edilen nem içeriği (Nİ), ayrılabilir nem oranı (ANO) ve kurutma hızı (KH) değerleri, Logsig Aktivasyon fonksiyonu ve Backpropagation öğrenme fonksiyonu kullanılarak 3 katmanlı yapay sinir ağları (YSA) ile modellenmiştir. Geliştirilen modelin istatistiksel geçerliliğinin belirlenmesinde ortalama kareli hata (OKH) kullanılmıştır. Sonuç olarak mevcut kurutma şartları için oluşturulan YSA ile mantarın kuruma davranışları başarılı bir şekilde tahmin edilmiştir.

Anahtar Kelimeler

Güneş kollektörü, Gıda kurutma, Tahminsel model, Yapay sinir ağları

Mushroom Drying in Air Heated Solar Collector Drying System and Modeling of Drying Performance with Artificial Neural Network

Öz

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the mushroom. In the experiments thinly sliced mushroom samples were used. Collector inlet and outlet air temperatures, drying chamber inlet and outlet air temperatures, ambient temperature, radiation, air velocity and drying rate were considered as parameters affecting the drying feature. The results obtained were presented as a function of drying time. Moisture content (MC), moisture ratio (MR) and drying rate (DR) values obtained from the experiments were modeled with 3-layer artificial neural network (ANN) using Logsig Activation function and Backpropagation learning function. Mean square error (MSE) was used to determine of the statistical validity of the developed model. As a result, drying behavior of mushroom was successfully predicted by ANN for existing drying conditions.

Anahtar Kelimeler

Solar Collector, Food drying, Predictive model, Artificial neural networks

Kaynakça

• Azadbakht, M., Aghili, H., Ziaratban, A., Torshizi, M. V. 2017. Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy, 120, 947-958.
• Beigi, M., Torki-Harchegani, M., Tohidi, M. 2017. Experimental and ANN modeling investigations of energy traits for rough rice drying. Energy, 141, 2196-2205.
• Chen, H., Hernandez, C. E., Huang , T. 2005. A study of the drying effect on lemon slices using a closed-type solar dryer. Solar Energy, Volume 78, Issue 97-103.
• Durmus, A., and Kurtbas, I. 2002. New designed air collector and collector drying efficiency of Elazig region with the help of apricot. IV. Engineering Architecture Symposium, Balıkesir.
• Findik, T., Tasdemir S., Şahin, I. 2010. The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders. Sci. Research and Essays, 5, 11, 1274-1283.
• Gulcimen, F., Karakaya, H., Durmus, A. 2016. Drying of sweet basil with solar air collectors. Renewable Energy, 93, 77-86.
• Gungor, A., Ozbalta, N. 2009. Dryer basics and industrial dryer course notes. IX. National Plumbing Engineering Congress. İzmir.
• Hajar, E., Rachid, T., Najib, B. M. 2017. Conception of a solar air collector for an indirect solar dryer. Energy Procedia, 141, 29-33.
• Kline, S.J., McClintock, F.A. 1953. Describing uncertainties in single-sample experiments. Mechanical engineering, 75, 3-8.
• Koni, M., Dincer, H., Turker, M. 2006. Modeling and control of drying processes of fermentation products. 11th National Congress of Electrical-Electronics-Computer Engineering, page 110.
• Sevik, S., Aktaş, M., Ozdemir, B. 2014. Modeling of drying behaviors of mushroom in a solar assisted heat pump dryer by using artificial neural network. Journal of Agrıcultural Scıences 20 (2014) 187-202.
• Tarhan, S., Ergunes, G., Tekelioglu, O. 2007. Design and operation principles of agricultural products for solar energy dryers. Installation Engineering Journal, Issue: 99, s.26-32.
• Teti, R., Jemielniak, K., O'Donnell, G., Dornfeld, D. 2010. Advanced monitoring of machining operations. Cirp Annals-Manufacturing Technology, 59 (2),717-739.
• Unal, A., Tanes, Y., Onur, H. Ş. 1986. The annual average sun exposure and the annual variation of temperature values are expressed by continuous functions. J. of Thermal Science and Technology, 8(4), 37-45.
• Winiczenko, R., Górnicki, K., Kaleta, A., Martynenko, A., Janaszek-Mańkowska, M. Trajer, J. 2018. Multi-objective optimization of convective drying of apple cubes. Computers and Electronics in Agriculture, 145, 341-348