The field of chemical engineering is witnessing a significant shift towards the integration of machine learning techniques to improve process optimization, predict thermodynamic properties, and enhance simulation accuracy. Researchers are leveraging machine learning algorithms to develop novel models and frameworks that can seamlessly integrate with existing simulation tools and experimental data. This has led to remarkable progress in predicting thermodynamic mixture properties, optimizing distillation columns, and improving the accuracy of complex thermochemical reaction processes. Notably, the development of reusable surrogate models and hybrid physics-data enrichments is enabling the creation of more accurate and computationally efficient models.

Some noteworthy papers in this regard include: A Machine Learning-Fueled Modelfluid for Flowsheet Optimization, which introduces a novel modelfluid representation for integrating ML-predicted data into flowsheet optimization. A machine-learned expression for the excess Gibbs energy, which presents a flexible neural network model for predicting the excess Gibbs energy of multi-component mixtures. Reusable Surrogate Models for Distillation Columns, which develops a reusable surrogate model for distillation columns that generalizes across a vast design space.