Vaporization properties are important for equipment modeling and process control involving liquid-vapor equilibrium. The aim of this work was to obtain an Artificial Neural Network (ANN) to predict volume, internal energy, enthalpy, and entropy of vaporization, and the saturation pressure (or temperature) of several fluids. Two strategies were proposed: one using saturation temperature in the inputs and another using saturation pressure. Five physicochemical descriptors were used to distinguish each substance. All ANNs were trained using the Levenberg-Marquardt method. Nine outputs combination were evaluated to obtain the best model, which was determined by simulating a dataset not shown in the training/validation/test steps. The results showed that ANNs with three outputs presented higher accuracy. The best one (with structure 4-40-40-3) predicted saturation pressure, internal energy and enthalpy of vaporization as outputs and presented relative errors as low as 0.02 %. Finally, we showed that ANNs can be reliable for vaporization properties prediction.

汽化特性对于涉及液体-蒸汽平衡的设备建模和过程控制很重要。这项工作的目的是获得一个人工神经网络（ANN），以预测汽化的体积，内部能量，焓和熵以及几种流体的饱和压力（或温度）。提出了两种策略：一种是在输入中使用饱和温度，另一种是使用饱和压力。五个理化指标用于区分每种物质。所有的人工神经网络都使用Levenberg-Marquardt方法进行了训练。评估了九种输出组合以获得最佳模型，该模型是通过模拟训练/验证/测试步骤中未显示的数据集确定的。结果表明，具有三项输出的人工神经网络具有较高的准确性。最好的（结构为4-40-40-3）预测饱和压力，内部能量和汽化焓作为输出，并且相对误差低至0.02％。最后，我们证明了人工神经网络对于汽化特性的预测是可靠的。