Recent advances and applications of surrogate models for finite element method computations: a review
J Kudela, R Matousek - Soft Computing, 2022 - Springer
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 …
increased popularity. Because of their capability to deal with black-box problems and lower …
Process systems engineering–the generation next?
Abstract Process Systems Engineering (PSE) is the scientific discipline of integrating scales
and components describing the behavior of a physicochemical system, via mathematical …
and components describing the behavior of a physicochemical system, via mathematical …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
A Bhosekar, M Ierapetritou - Computers & Chemical Engineering, 2018 - Elsevier
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 …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
OMLT: Optimization & machine learning toolkit
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 …
incorporating neural network and gradient-boosted tree surrogate models, which have been …
Overview of surrogate modeling in chemical process engineering
K McBride, K Sundmacher - Chemie Ingenieur Technik, 2019 - Wiley Online Library
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 …
growing success and efficiency in process design and operation. These improvements …
Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective
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 …
fossil-based power and industrial sectors and is a bridging technology for a sustainable …
[HTML][HTML] Formulating data-driven surrogate models for process optimization
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 …
research into data-driven modeling for mathematical optimization of process applications …
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
SH Kim, F Boukouvala - Optimization Letters, 2020 - Springer
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 …
attracted significant attention in the recent literature. A very efficient approach for optimizing …
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
EA del Rio Chanona, P Petsagkourakis… - Computers & Chemical …, 2021 - Elsevier
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 …
model mismatch in real-time optimization of uncertain processes. The main contribution lies …
[HTML][HTML] Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …
sequential setting of Bayesian Optimization does not translate well into laboratory …