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Welcome to Machine Learning in Chemical Engineering! 👋

Introduction

An overview of the course 🔭
1. Introduction to Python 🐍

Supervised learning

2. kNN for (Q)SPR modeling ⚛️
3. GLM for thermophysical property prediction ⚗️
4. Deep Neural Networks for VLE prediction 🌐

Hybrid modelling

5. Hybrid model for CSTR 🔆

Data-driven optimization

6. PID tuning via data-driven optimization 🔩

Reinforcement learning

7. Reinforcement learning for Control 🐶

Unsupervised learning

8. Process monitoring using PCA 📐
9. Clustering for energy systems modelling 📥

Acknowledgments

Acknowledgements 🙏

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By Edgar Ivan Sanchez Medina, Antonio del Rio Chanona and Caroline Ganzer

© Copyright 2023.