separationML

Source code and trained models for the paper "Physics-informed Data-driven control of Electrochemical Separation Processes".

Set-up environment

Clone this repository and then use setup.sh to setup a virtual environment separation with the required dependencies in requirements.txt.

chmod +x setup.sh
git clone https://github.com/EnthusiasticTeslim/separationML.git
cd separationML
sh env.sh
source binfo/bin/activate

Training RL

The notebooks for training the RL-based controllers are contained here. More information on RL environment is available logic.md.

Important

All modules in setting up the RL controller are available in OptiDial.

How-to-cite

Cite the paper using the following:

@article{doi,
  author = {Teslim Olayiwola, Kyle Terito, Jose Romagnoli},
  title = {Physics-informed Data-driven control of Electrochemical Separation Processes},
  journal = {n/a},
  year = {n/a},
  volume = {n/a},
  number = {n/a},
  doi = {https://doi.org/}
}

References

• Ortiz et al (2005). Brackish Water Desalination by Electrodialysis: Batch Recirculation Operation Modeling, J. Membr. Sci. 2005, 252 (1), 65–75.

• Teslim et al (2024). Synergizing Data-Driven and Knowledge-Based Hybrid Models for Ionic Separations, ACS EST Engg. 2024