Welcome to Machine Learning in Chemical Engineering! 👋#

This book 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 and the Optimisation and Machine Learning for Process Systems Engineering group at Imperial College London to share experiences and material used in the respective MLCE courses offered in these institutions.

Check out the content of the book:

Introduction

Supervised learning

Hybrid modelling

Data-driven optimization

Reinforcement learning

Unsupervised learning

Acknowledgments

This book was prepared by Edgar Ivan Sanchez Medina, Antonio del Rio Chanona and Caroline Ganzer with the help of many collegues. If you have a question or need some help in adapting this book to your MLCE course feel free to contact us!

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; and Ganzer, Caroline. (2023). Machine Learning in Chemical Engineering (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7986905

We are also very grateful to the JupyterBook project which allows this materials to be developed in a relatively simple way!