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

Year 2020, , 593 - 609, 15.06.2020

Mehmet Daş , Ebru Kavak Akpınar

https://doi.org/10.24012/dumf.585021

Cited By: 1

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.

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

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