Estimation of Moist Air Thermodynamic Properties using Artificial Neural Network

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.