Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks

TL;DR

This paper develops neural network models to accurately predict vapor-liquid equilibrium of long-chain n-alkane/nitrogen mixtures at high pressures, outperforming traditional equations of state and capturing critical behavior.

Contribution

The study introduces two neural network-based models for VLE prediction of n-alkane/nitrogen mixtures, demonstrating superior accuracy over Peng-Robinson EOS and capturing critical phenomena.

Findings

Neural network models outperform PR-EOS in predicting equilibrium pressure.

Models accurately trace curvature changes near critical points.

Predictions remain accurate up to 60 MPa pressure.

Abstract

Understanding fluid phase behavior in high pressure and high temperature conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems. The study of vapor-liquid equilibrium (VLE) curves also forms an integral part of the design and modeling of the control processes in chemical and oil-gas industries. The main objective of this study was to develop data-driven models to predict VLE of Type III binary mixtures involving long-chained n-alkanes and nitrogen. Two data-driven models have been proposed in this study, each of which was competent in estimating VLE for the binary systems of C10/N2 and C12/N2, at pressures ranging up to 50-60 MPa. Both the models showed better performance (less average absolute percentage error) in predicting equilibrium pressure of the binary mixtures as compared to the VLE modeled using Peng-Robinson equation of state (PR-EOS). The data-driven models were also able to correctly trace the change in curvature of the vapor phase composition, close to the mixture critical point, at high pressures and temperatures-a feature of the n-alkane/nitrogen system VLE which the PR-EOS model fails to capture.