Abstract 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.

中文翻译：

---

摩尔密度和水比热的神经网络相关性：压力高达 100 MPa 时的预测

摘要 IAPWS-95 公式是一组高精度计算水属性的方程。为了获得某种热力学性质，需要一个或多个迭代程序，这需要大量的计算工作。这项工作的目的是获得一个基于人工神经网络的方程来预测 IAPWS-95 公式中液体、蒸汽和超临界相中水的密度 (ρ) 和比热 (Cp) 的公式值，包括饱和线。考虑了温度高达 1275 K 和压力高达 100 MPa 下的数据。测试了不同的输入变量集，并使用以下各项获得了最佳结果：温度、压力和声速（用于区分饱和线处的液体和蒸汽）。网络 3-20-15-2 比 IAPWS 公式快 99.70%，