https://github.com/edgarsmdn/MLCE_book
Hands-on material for a Machine Learning in Chemical Engineering course
https://github.com/edgarsmdn/MLCE_book
chemical-engineering deep-learning jupyter-book machine-learning process-systems-engineering python
Last synced: 4 months ago
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Hands-on material for a Machine Learning in Chemical Engineering course
- Host: GitHub
- URL: https://github.com/edgarsmdn/MLCE_book
- Owner: edgarsmdn
- License: apache-2.0
- Created: 2023-02-28T09:38:42.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-18T19:51:37.000Z (over 1 year ago)
- Last Synced: 2023-12-16T15:56:25.176Z (over 1 year ago)
- Topics: chemical-engineering, deep-learning, jupyter-book, machine-learning, process-systems-engineering, python
- Language: Jupyter Notebook
- Homepage: https://edgarsmdn.github.io/MLCE_book/
- Size: 51 MB
- Stars: 56
- Watchers: 5
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning in Chemical Engineering
This repo contains the building material for a [JupyterBook](https://jupyterbook.org/en/stable/intro.html) which is intended to serve as a template/prototype for the hands-on part of a Machine Learning in Chemical Engineering (MLCE) course. This was a collective effort between the [Process Systems Engineering group at the Otto von Guericke University / MPI Magdeburg](https://www.mpi-magdeburg.mpg.de/2316/en) and the [Optimisation and Machine Learning for Process Systems Engineering group at Imperial College London](https://www.imperial.ac.uk/optimisation-and-machine-learning-for-process-engineering/about-us/) to share experiences and material used in the respective MLCE courses offered in these institutions.
### To look at the book 📚💻 go to [this link](https://edgarsmdn.github.io/MLCE_book/intro.html)!
## Contents
The book aims at covering application case-studies in chemical engineering of
- **Supervised learning**
- **Unsupervised learning**
- **Reinforcement learning**
- **Data-driven optimization**
- **Hybrid modelling**
## Do you want to contribute?
If you have nice tutorials in the areas mentioned above reflecting case-studies in chemical engineering, we encourage you to share it with the community! 💪 For practical reasons, it is better if you submit your pull-request including a link to a working Colab Notebook.
## Did you notice an error/typo?
Let us know! Submit your issue here and we will fix it. We encourage you to contribute to this resource!
## Citation
To cite this JupyterBook use
```
@book{sanchez_chanona_ganzer_2023,
title = {Machine Learning in Chemical Engineering},
author = {Sanchez Medina, Edgar Ivan and del Rio Chanona, Ehecatl Antonio and Ganzer, Caroline},
year = {2023},
publisher = {JupyterBook},
url = {https://edgarsmdn.github.io/MLCE_book/},
DOI = {10.5281/zenodo.7986905}
}
```
or perhaps the more conventional:
- Sanchez Medina, Edgar Ivan, del Rio Chanona, Ehecatl Antonio, & Ganzer, Caroline. (2023). Machine Learning in Chemical Engineering (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7986905
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