Synergizing Data-Driven and Knowledge-based hybrid models for Ionic Separation

Welcome to this repository! It encompasses code and resources tied to knowledge-informed machine learning for electrochemical separation processes.

Directory Structure

1. PhysicModel.ipynb: The model showing the implementation of physics model for the 3 different EDI dataset
2. DataGeneration.ipynb: Illustrates the sensitivity of the physics model to different input variables.
3. ResinModel_Xgboost.ipynb: ML model for the resin conductivity
4. transfer-learning-EOS-models.ipynb: transfer learning using the simulated and experimental dataset.
5. DevOpt-EOS-tensorflow.ipynb: multiobjective optimization framework.

Citation

If you find the work useful in your research, kindly cite the following:

@article{doi,
  author = {Teslim Olayiwola; Luis A. Briceno-Mena; Christopher G. Arges; Jose A. Romagnoli},
  title = {Synergizing Data-Driven and Knowledge-based hybrid models for Ionic Separation},
  journal = {Submitted to ACS ES&T Engineering},
  year = {n/a},
  volume = {n/a},
  number = {n/a},
  doi = {https://doi.org/},
  preprint = {https://chemrxiv.org/engage/chemrxiv/article-details/6674a85c5101a2ffa81a2b24}
}

License

This project is licensed under the MIT License.