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co2_in_h2o_diffusivity

https://github.com/ggravanis/co2_in_h2o_diffusivity

Science Score: 31.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: ggravanis
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 85 KB
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

CO2 in H2O diffusivity

This is a public repository to host the source code and the data to create models for predicting the diffusivity of CO2 in H2O.

Citation

If you use the source code or the complete CO2 in H2O dataset please cite the following article:

Gravanis, G., Papadopoulou, S., Voutetakis, S., Diamantaras, K., & Tsimpanogiannis, I. N. (2024). A machine learning approach to predict CO2 diffusivity in liquid H2O over a wide pressure and temperature range. Fluid Phase Equilibria, 114325.

Owner

  • Login: ggravanis
  • Kind: user

Citation (citation.bib) 

@article{GRAVANIS2025114325,
title = {A machine learning approach to predict CO2 diffusivity in liquid H2O over a wide pressure and temperature range},
journal = {Fluid Phase Equilibria},
volume = {592},
pages = {114325},
year = {2025},
issn = {0378-3812},
doi = {https://doi.org/10.1016/j.fluid.2024.114325},
url = {https://www.sciencedirect.com/science/article/pii/S0378381224003005},
author = {Georgios Gravanis and Simira Papadopoulou and Spyros Voutetakis and Konstantinos Diamantaras and Ioannis N. Tsimpanogiannis},
keywords = {CO diffusivity prediction, Experimental dataset, Machine learning, Autoencoders},
abstract = {This study presents a machine learning approach for predicting the diffusivity of CO2 in liquid H2O over a wide range of temperatures and pressures. A comprehensive experimental dataset is compiled, including over 300 data points from existing literature, as well as, 75 newly identified diffusivity measurements. These data span a broad spectrum of temperatures and pressures. Various machine learning models namely, Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (kNN), and Autoencoders, are trained on this enhanced dataset and evaluated for their accuracy in diffusivity prediction. Results show that the Autoencoder model achieves superior performance, accurately predicting CO2 diffusivity even in regions where experimental data is sparse. The model’s ability to generalize across a wide range of temperatures and pressures, demonstrates its potential for use in real-world applications, enabling fast, reliable predictions with minimized computational cost.}
}

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