Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
Modelling for digital twins—potential role of surrogate models
The application of white box models in digital twins is often hindered by missing knowledge,
uncertain information and computational difficulties. Our aim was to overview the difficulties …
uncertain information and computational difficulties. Our aim was to overview the difficulties …
[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 …
Sustainable design, integration, and operation for energy high-performance process systems
The worldwide energy demands and resource consumption are rising despite the efforts for
energy saving and emission reduction. This results from the combination of the supply chain …
energy saving and emission reduction. This results from the combination of the supply chain …
[HTML][HTML] Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
We propose a novel method enabling autocompletion of chemical flowsheets. This idea is
inspired by the autocompletion of text. We represent flowsheets as strings using the text …
inspired by the autocompletion of text. We represent flowsheets as strings using the text …
A surrogate-assisted differential evolution for expensive constrained optimization problems involving mixed-integer variables
Abstract Many Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been developed
for expensive constrained optimization problems (ECOPs) with continuous variables …
for expensive constrained optimization problems (ECOPs) with continuous variables …
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 …
Constrained discrete black-box optimization using mixed-integer programming
TP Papalexopoulos, C Tjandraatmadja… - International …, 2022 - proceedings.mlr.press
Discrete black-box optimization problems are challenging for model-based optimization
(MBO) algorithms, such as Bayesian optimization, due to the size of the search space and …
(MBO) algorithms, such as Bayesian optimization, due to the size of the search space and …
Generative ai and process systems engineering: The next frontier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …
Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty
The coordination of interconnected elements across the different layers of the supply chain
is essential for all industrial processes and the key to optimal decision-making. Yet, the …
is essential for all industrial processes and the key to optimal decision-making. Yet, the …