This is an hands-on workshop for Chemical Reaction Engineering Python application, including parameters identification and machine learning model training.
This workshop is tailored for chemical engineers who are new to Python and machine learning. It focuses on the resolution of chemical reaction engineering problems using Python tools and machine learning model training for complex reaction behaviour prediction.
Python Essentials: Learn the basics of Python programming, data manipulation, and visualization.
ML for Process Capability Prediction: Resolve dynamic equation typical of reaction engineering using Python code.
ML for Property Prediction: Apply regression techniques to estimate reaction rate and properties using machine learning techniques.
All notebooks in this repository are designed to run directly in Google Colab — no installation required! Just download the repository and upload it in Colab to run the Jupyter notebook. Each exercise is provided in two versions:
- 📝 Workbook: A guided notebook with tasks and empty cells for participants to complete.
- ✅ Solution: A fully worked-out version with explanations and code
We gratefully acknowledge the contributions of the following individuals who helped design, develop, and deliver the workshop: Ulderico Di Caprio, Leone Mazzeo and Vincenzo Piemonte. The workshop is inspired by KU Leuven ChemML workshop.
Chemical Reaction Engineering in Python. A practical course designed for chemical engineers that want to learn Python. It covers various topics, such as basic Python syntax, solving ODEs, stoichiometry, data visualization and basic data analysis.
KU Leuven ChemML. A hands-on workshop designed to introduce chemical engineers to the fundamentals of machine learning with practical applications in process modeling and property prediction
Machine Learning in Chemical Engineering. Large course on advanced Python and ML application on chemical engineering problems.