Work

Solubility Prediction in N2O + Ionic Liquid Systems Using Artificial Neural Networks Including Thermodynamically Consistent Data

Year: 2025

Type: article

Abstract: The solubilities of 498 datasets of N2O and ionic liquid systems were predicted using a multilayer perceptron. The data used to train the artificial neural network was subjected to the Gibbs–Duhem test to analyze their thermodynamic consistency. The Peng–Robinson cubic equation of state, combined with the Kwak–Mansoori mixing rule, was used as the thermodynamic model to implement the test. The analysis indicated that 71.9% of the data were declared thermodynamically inconsistent. The ability of artificial neural networks (ANNs) to predict the solubility of these systems using experimental datasets that do not satisfy the thermodynamic consistency criteria based on the Gibbs–Duhem equation was studied. The multilayer perceptron model achieved an average absolute deviation of 1.81% and a maximum individual deviation of 7.56%. These results highlight the potential of ANNs as robust predictive tools even when the available data do not fully satisfy thermodynamic consistency criteria.

Source: Processes

DOI: 10.3390/pr13124072

Authors: Elías N. Fierro, Ariana S. Muñoz, Patricio I. Cerda, Claudio A. Faúndez

Institutions: University of Concepción, Universidad Complutense de Madrid, Universidad Autónoma de Chile

Language: English

Topic: Phase Equilibria and Thermodynamics

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Agencia Nacional de Investigación y Desarrollo