Work

XGBoost and Artificial Neural Networks as Surrogate Models for Vapor–Liquid Equilibrium in PC-SAFT

Year: 2025

Type: article

Abstract: Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the input–output relationships of more complex, computationally demanding models. This work employs XGBoost and a hybrid XGBoost-artificial neural networks (XGBoost-ANN) model as surrogate models to replace PC-SAFT EoS calculations for the vapor–liquid equilibrium (VLE) of binary associating systems. This work investigates the VLE of five binary associating systems using data generated by the PC-SAFT EoS. The surrogate models take temperature, pressure, liquid phase mole fractions, and the PC-SAFT parameters for binary associating systems as inputs, and predict the vapor phase mole fractions. Both surrogate models significantly reduce the computational time for calculating VLE data compared to the PC-SAFT EoS, while achieving good prediction results.

Source: Processes

DOI: 10.3390/pr13123918

Authors: Yiwen Pang, Zhongwei Ding, Qunsheng Li

Institution: Beijing University of Chemical Technology

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: National Key Research and Development Program of China