Estimation of Moist Air Thermodynamic Properties using Artificial Neural Network

Year 2016, Volume: 31 Issue: 1, 51 - 58, 15.06.2016

Arif Ozbek

https://doi.org/10.21605/cukurovaummfd.317724

Cited By: 3

Abstract

In this study, the equations obtained non-iteratively are presented for moist air thermodynamic properties as a function of dry-bulb temperature and relative humidity. In this regard, an artificial neural network (ANN) was performed by using MATLAB software. In the ANN, dry-bulb temperature and relative humidity were specified as inputs, and water vapor saturation and partial pressures, wet-bulb and dew-point temperatures were determined as outputs. The sensitivity of the neural network performance was also controlled, and acceptable accuracy was obtained for all estimations for practical applications. The moist air thermodynamic properties can be alternatively estimated with the mean absolute percentage error (MAPE) of less than 0,5% by using the developed model. With respect to the acquired results, this model supplies simple and correct predictions to specify moist air thermodynamic properties non-iteratively. Determination of moist air thermodynamic properties using ANN approach is a good alternative to some other mathematical models.

Keywords

Air thermodynamic properties, Dry-bulb temperature, Wet-bulb temperature, Relative humidity, Artificial Neural Network (ANN)

Yapay Sinir Ağları Kullanarak Nemli Havanın Termodinamik Özelliklerinin Tahmini

Abstract

Bu çalışmada, nemli havanın termodinamik özellikleri kuru termometre sıcaklığı ve bağıl nemin bir fonksiyonu olarak iterasyona gerek olmadan eşitlikler ile sunulmuştur. Bu amaçla, MATLAB programı kullanılarak yapay sinir ağları metodu uygulanmıştır. Bu metotta kuru termometre sıcaklığı ve bağıl nem girdi verisi olarak kullanılırken; su buharının doyma ve kısmi basınçları ile yaş termometre ve çiğ noktası sıcaklıkları da çıktı olarak hesaplanmıştır. Yapay sinir ağları hassasiyeti ile beraber hesaplamalardaki doğruluklar da kontrol edilmiştir. Kullanılan model ile nemli havanın termodinamik özellikleri 0,5’ten daha düşük ortalama mutlak yüzde hata değeri ile hesaplanmıştır. Elde edilen değerlere göre bu model iterasyona gerek olmadan nemli havanın termodinamik özelliklerini belirlemede basit ve doğru tahminler sunmaktadır. Yapay sinir ağları kullanarak nemli havanın termodinamik özelliklerinin tespiti diğer

matematik modellere iyi bir alternatif oluşturmaktadır.

Keywords

Havanın termodinamik özellikleri, Kuru termometre sıcaklığı, Yaş termometre sıcaklığı, Bağıl nem, Yapay sinir ağları

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