Overview of surrogate modeling in chemical process engineering
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 …
[HTML][HTML] A systematic review of machine learning approaches in carbon capture applications
Climate change and global warming are among of the most important environmental issues
and require adequate and immediate global action to preserve the planet for future …
and require adequate and immediate global action to preserve the planet for future …
Adaptive sequential sampling for surrogate model generation with artificial neural networks
Surrogate models–simple functional approximations of complex models–can facilitate
engineering analysis of complicated systems by greatly reducing computational expense …
engineering analysis of complicated systems by greatly reducing computational expense …
Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN
In this research, artificial neural networks (ANN) and response surface methodology (RSM)
were applied for modeling and optimization of carbon dioxide (CO2) absorption using KOH …
were applied for modeling and optimization of carbon dioxide (CO2) absorption using KOH …
A framework of hybrid model development with identification of plant‐model mismatch
Hybrid modeling has attracted increasing attention in order to take advantage of the
additional data to improve process understanding. Current practice often adopts mechanistic …
additional data to improve process understanding. Current practice often adopts mechanistic …
Mathematical programming for piecewise linear regression analysis
In data mining, regression analysis is a computational tool that predicts continuous output
variables from a number of independent input variables, by approximating their complex …
variables from a number of independent input variables, by approximating their complex …
An adaptive machine learning method based on finite element analysis for ultra low-k chip package design
Machine learning (ML) is widely used for building data-driven models that are highly useful
for optimization. In this study, a finite element model-based adaptive ML method is …
for optimization. In this study, a finite element model-based adaptive ML method is …
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 …
Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis
Sampling techniques are used in many fields, including design of experiments, image
processing, and graphics. The techniques in each field are designed to meet the constraints …
processing, and graphics. The techniques in each field are designed to meet the constraints …
Optimization of CO2 capture process with aqueous amines using response surface methodology
Amine is one of candidate solvents that can be used for CO2 recovery from the flue gas by
conventional chemical absorption/desorption process. In this work, we analyzed the impact …
conventional chemical absorption/desorption process. In this work, we analyzed the impact …