Estimation of vaporization properties of pure substances using artificial neural networks. (15th February 2021)

• Record Type: Journal Article
• Authors: Ottaiano, Gabriel Y.; da Cruz, Isabela N.S.; da Cruz, Higor S.; Martins, Tiago D.
• Is Part Of: Chemical engineering science. Volume 231(2021)
• Publication Date: 2021-02-15
• Subjects: Artificial intelligence -- Vapor-liquid equilibria -- Thermodynamic properties -- Prediction; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2020.116324
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: The estimation of vaporization properties using artificial neural networks were evaluated. One ANN for prediction of vaporization properties of different substances was obtained. One ANN was capable to predict several vaporization properties with high accuracy. ANN results were more accurate than those calculated by correlations or equations of state. Abstract: 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 (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.