This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Membrane technology is a promising next-generation gas separation technology and has
drawn tremendous research interest during the past decades. Despite the advanced …

The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

Many of the processes in chemical engineering applications are of dynamic nature.
Mechanistic modeling of these processes is challenging due to the complexity and …

Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

This paper proposes a new hybrid strategy to optimally design membrane separation
problems. We formulate the problem as a Non-Linear Programming (NLP) model. A …

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning
and neural architecture search, as they achieve good predictive performance with little or no …

The past decade has seen a sharp increase in machine learning (ML) applications in
scientific research. This review introduces the basic constituents of ML, including databases …