Calculation of some exergetic indicators in a solar dryer and modeling with artificial neural network

Abstract

Thermodynamic analysis, especially exergy analysis, is an important tool for analysis of thermal systems. Many formulations and data are used for exergy calculations in drying systems. Today, it is difficult to process and analyze a large amount of data manually. Therefore, in order to solve a given problem, it is aimed to reach the solution by educating the data obtained from the problem environment with artificial intelligence methods. In this study, apple product was dried in a solar drying system and exergy analysis of the drying process of the product was carried out. The effects of some exergetic indicators on the performance of drying system used in apple product drying were investigated. For this purpose, exergetic indicators such as exergy effect, waste exergy ratio, environmental impact factor, external sustainability index and improvement potential have been taken into consideration. A predictive model was constructed using the artificial neural network to estimate the waste exergy rate, which is an exergetic indicator.  Mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) error analyzes were used to calculate the validity of the model. As a result, the waste exergy ratio increased as the drying time increased. Exergy efficiency and improvement potential of solar drying system decreased with increasing drying time. The predictive model created using ANN has successfully predicted the rate of waste exergy ratio. It has been shown that the resulting predictive model can be used for different drying systems and different products.

Keywords

• Solar drying,Exergetic indicators,Waste exergy ratio,Artificial neural network

Project Number

MF-16.34

Güneş enerjili bir kurutucudaki ekserjetik faktörlerin hesaplanması ve yapay sinir ağı ile modellenmesi

Abstract

Termodinamik analiz, özellikle ekserji analizi, termal sistemlerin analizi için önemli bir araçtır. Kurutma sistemlerinde ekserji hesaplamaları için birçok formülasyon ve veri kullanılmaktadır. Bugün, büyük miktarda veriyi elle işlemek ve analiz etmek zordur. Bu nedenle, verilen bir problemi çözmek için problem ortamından elde edilen verileri yapay zeka yöntemleri ile eğiterek çözüme ulaşmak hedeflenmektedir. Bu çalışmada, elma ürünü bir güneş kurutma sisteminde kurutuldu ve ürünün kurutma işleminin ekserji analizi yapıldı. Bazı ekserjetik faktörlerin elma ürünü kurutmasında kullanılan kurutma sisteminin performansı üzerine etkileri incelenmiştir. Bu amaçla, ekserji etkisi, atık ekserji oranı (AEO), çevresel etki faktörü (ÇEF), ekserjetik sürdürülebilirlik indeksi (ESI) ve iyileştirme potansiyeli (IP) gibi ekserjetik faktörler dikkate alınmıştır. Eksergetik bir faktör olan AEO değerlerini tahmin etmek için yapay sinir ağı kullanılarak öngörücü bir model oluşturulmuştur. Modelin geçerliliğini hesaplamak için ortalama mutlak hata (MAE), kök ortalama kare hatası (RMSE), göreceli mutlak hata (RAE) ve kök göreceli mutlak hata (RRAE) hata analizleri kullanılmıştır. Sonuç olarak, kuruma süresi arttıkça AEO artmıştır. Güneş enerjisi kurutma sisteminin ekserji verimliliği ve gelişme potansiyeli, kuruma süresi arttıkça azalmıştır. YSA kullanılarak oluşturulan öngörücü model, AEO değerlerini başarıyla öngörmüştür. Elde edilen öngörü modelinin farklı kurutma sistemleri ve farklı ürünler için kullanılabileceği gösterilmiştir.

Keywords

• Güneş kurutucu,Ekserjetik faktörler,Atık ekserji oranı,Yapay sinir ağı

Supporting Institution

FUBAP

Project Number

MF-16.34

Thanks

Yazarlar, MF-16.34 proje numarası altında, Fırat Üniversitesi Bilimsel Araştırma Proje birimine (FUBAP) teşekkür ederler.

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