Estimation of Moist Air Thermodynamic Properties using Artificial Neural Network

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

Yapay Sinir Ağları Kullanarak Nemli Havanın Termodinamik Özelliklerinin Tahmini

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 dewpoint 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 noniteratively. Determination of moist air thermodynamic properties using ANN approach is a good alternative to some other mathematical models.

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1.Sreekanth, S., Ramaswamy, H.S., Sablani, S. 1998. Prediction of Psychrometric Parameters Using Neural Networks, Drying Technology: An International J. 16: 825-837.
2.Bialobrzewski, I. 2008. Neural Modeling of Relative Air Humidity. Computers and Electronics in Agric. 60: 1-7.
3.De, S.S., Debnath, A. 2009. Artificial Neural Network Based Predictionof Maximum and Minimum Temperature in the Summer Monsoon Months Over India. Applied Physics Research 1: 37-44.
4.Joshi, P., Ganju, A. 2012. Maximum and Minimum Temperature Prediction Over Western Himalaya Using Artificial Neural Network. Mausam. 63: 283-290.
5.Kisi, O., Kim, S., Shiri, J. 2013. Estimation of Dew Point Temperature Using Neuro-Fuzzy and Neural Network Techniques. Theoretical and Applied Climatology 114: 365-373.
6.Kuligowski. R.J., Barros, A.P. 1998. Localized Precipitation Forecasts from aNumerical Weather Prediction Model Using Artificial Neural Networks. Weather and Forecasting 13: 1194-1204.
7.Mittal, G.S. 2003. Zhang J Artificial Neural Network-Based Psychrometric Predictor. Biosystems Engineering 85: 283-289.
8.Wilhelm, L.R. 1976. Numerical Calculation of Psycrometric Properties in SI units. Transactions of the ASAE 19: 318-321.
9.Singh, A.K., Singh, H., Singh, S.P., Sawhney, R.L. 2002. Numerical Calculation of Psychrometric Properties on aCalculator. Building and Environment 37: 415-419.
10.ASHRAE. 1993. ASHRAE Handbook Fundamentals. ASHRAE. Atlanta.
11.Tabari, H., Kisi, O., Ezani, A., Talaee, P.H. 2012. SVM, ANFIS, Regression and Climate Based Models for Reference Evapotranspiration Modeling Using Limited Climatic Data in aSemi-Arid Highland Environment. Journal of Hydrology 444-445:78-89.
12.Bilgili, M. 2010. Prediction of Soil Temperature Using Regression and Artificial Neural Network Models. Meteorology and Atmospheric Physics 110: 59-70.
13.Hosoz, M., Ertunc, H.M., Bulgurcu, H. 2007. Performance Prediction of aCooling Tower Using Artificial Neural Network. Energy Conversion and Management 48: 1349-1359.
14.Senkal, O., Yildiz, B.Y., Sahin, M., Pestemalci, V. 2012. Precipitable Water Modelling Using Artificial Neural Network in Çukurova Region. Environmental Monitoring and Assessment 184: 141-147.