Machine learning in process systems engineering: Challenges and opportunities
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 …
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
Machine learning in gas separation membrane developing: Ready for prime time
Membrane technology is a promising next-generation gas separation technology and has
drawn tremendous research interest during the past decades. Despite the advanced …
drawn tremendous research interest during the past decades. Despite the advanced …
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 …
[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 …
Physics-informed recurrent neural networks and hyper-parameter optimization for dynamic process systems
T Asrav, E Aydin - Computers & Chemical Engineering, 2023 - Elsevier
Many of the processes in chemical engineering applications are of dynamic nature.
Mechanistic modeling of these processes is challenging due to the complexity and …
Mechanistic modeling of these processes is challenging due to the complexity and …
The application of physics-informed machine learning in multiphysics modeling in chemical engineering
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 …
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
[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 …
Data augmentation driven by optimization for membrane separation process synthesis
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 …
problems. We formulate the problem as a Non-Linear Programming (NLP) model. A …
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
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 …
and neural architecture search, as they achieve good predictive performance with little or no …
[HTML][HTML] Machine learning for chemistry: basics and applications
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 …
scientific research. This review introduces the basic constituents of ML, including databases …