The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

Abstract Process Systems Engineering (PSE) is the scientific discipline of integrating scales
and components describing the behavior of a physicochemical system, via mathematical …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

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 …

The ability to accurately model and simulate chemical processes has been paramount to the
growing success and efficiency in process design and operation. These improvements …

Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize
fossil-based power and industrial sectors and is a bridging technology for a sustainable …

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 …

Optimization of simulation-based or data-driven systems is a challenging task, which has
attracted significant attention in the recent literature. A very efficient approach for optimizing …

This paper investigates a new class of modifier-adaptation schemes to overcome plant-
model mismatch in real-time optimization of uncertain processes. The main contribution lies …

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