{"id":15074746,"url":"https://github.com/edgarsmdn/MLCE_book","last_synced_at":"2025-09-25T22:31:04.366Z","repository":{"id":144158181,"uuid":"607593694","full_name":"edgarsmdn/MLCE_book","owner":"edgarsmdn","description":"Hands-on material for a Machine Learning in Chemical Engineering course","archived":false,"fork":false,"pushed_at":"2023-08-18T19:51:37.000Z","size":53440,"stargazers_count":56,"open_issues_count":0,"forks_count":13,"subscribers_count":5,"default_branch":"main","last_synced_at":"2023-12-16T15:56:25.176Z","etag":null,"topics":["chemical-engineering","deep-learning","jupyter-book","machine-learning","process-systems-engineering","python"],"latest_commit_sha":null,"homepage":"https://edgarsmdn.github.io/MLCE_book/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/edgarsmdn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-02-28T09:38:42.000Z","updated_at":"2023-12-20T16:49:55.174Z","dependencies_parsed_at":"2023-12-20T16:49:51.637Z","dependency_job_id":"5f5025a5-a1d0-4220-a69a-c5fef787912d","html_url":"https://github.com/edgarsmdn/MLCE_book","commit_stats":{"total_commits":35,"total_committers":3,"mean_commits":"11.666666666666666","dds":"0.22857142857142854","last_synced_commit":"b0bf0992af517340c72cf4f863042106f8125839"},"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edgarsmdn%2FMLCE_book","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edgarsmdn%2FMLCE_book/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edgarsmdn%2FMLCE_book/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edgarsmdn%2FMLCE_book/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/edgarsmdn","download_url":"https://codeload.github.com/edgarsmdn/MLCE_book/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":219871405,"owners_count":16554408,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chemical-engineering","deep-learning","jupyter-book","machine-learning","process-systems-engineering","python"],"created_at":"2024-09-25T03:47:46.036Z","updated_at":"2025-09-25T22:30:55.243Z","avatar_url":"https://github.com/edgarsmdn.png","language":"Jupyter Notebook","funding_links":[],"categories":["Machine Learning"],"sub_categories":[],"readme":"# Machine Learning in Chemical Engineering\n\nThis 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.\n\n### To look at the book 📚💻 go to [this link](https://edgarsmdn.github.io/MLCE_book/intro.html)!\n\n## Contents\n\nThe book aims at covering application case-studies in chemical engineering of \n\n- **Supervised learning**\n- **Unsupervised learning** \n- **Reinforcement learning** \n- **Data-driven optimization**\n- **Hybrid modelling**\n\n## Do you want to contribute?\n\nIf 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.\n\n## Did you notice an error/typo?\n\nLet us know! Submit your issue here and we will fix it. We encourage you to contribute to this resource!\n\n## Citation\n\nTo cite this JupyterBook use\n\n```\n@book{sanchez_chanona_ganzer_2023,\n    title = {Machine Learning in Chemical Engineering},\n    author = {Sanchez Medina, Edgar Ivan and del Rio Chanona, Ehecatl Antonio and Ganzer, Caroline},\n    year = {2023},\n    publisher = {JupyterBook},\n    url = {https://edgarsmdn.github.io/MLCE_book/},\n    DOI = {10.5281/zenodo.7986905}\n}\n```\n\nor perhaps the more conventional:\n\n- Sanchez Medina, Edgar Ivan, del Rio Chanona, Ehecatl Antonio, \u0026 Ganzer, Caroline. (2023). Machine Learning in Chemical Engineering (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7986905\n\n#### If you find this material useful give it a star ⭐ so that it can, potentially, help more people \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedgarsmdn%2FMLCE_book","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fedgarsmdn%2FMLCE_book","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedgarsmdn%2FMLCE_book/lists"}