The IAPWS-95 formulation is a set of equations that calculates water properties with high precision. To obtain a certain thermodynamic property, one or more iterative procedures are needed, which demand high computational effort. The aim of this work was to obtain an Artificial Neural Network based equation to predict the IAPWS-95 formulation values of density (ρ) and specific heat (Cp) of water in liquid, vapor, and supercritical phases, including the saturation line. Data at temperatures up to 1275 K, and pressures up to 100 MPa were considered. Different sets of input variables were tested and best results were obtained using: temperature, pressure, and speed of sound (used to differentiate liquid from vapor at the saturation line). The network 3-20-15-2 was 99.70% faster than the IAPWS formulation, and presented an overall mean percentage errors of 0.23% and 0.51% for ρ and Cp, respectively, which were lower than those obtained using known correlations.
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