Github, GitLab and Google Colab Notebooks

Github: Optimisation and Machine Learning for Process Engineering

Here you can find some of our Github, GitLab and Google Colaboratory Notebooks

Derivative-free optimisation by Tom Savage

Evolutionary Algorithms

Particle Swarm Optimization

RBF Functions for Surrogate Optimization

Surrogate Optimization using Artificial Neural Networks

McCabe-Thiele Method with Murphree Efficiency using Python

Reinforcement Learning by Ilya Orson Sandoval

Reinforcement Learning for batch processes optimisation using Proximal Policy Optimization (PPO) and Reinforce

This repository is an example of the methods described in: Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation

 

 

 

 

 

 

 

 

 

 

 

 

Reinforcement Learning for nonsmooth process optimisation by Panos Petsagkourakis

Reinforcement Learning for Batch Bioprocess Optimisation (applied to non-smooth systems)

This application is a worked-example using the methods described in Reinforcement Learning for Batch Bioprocess Optimization

 

 

 

 

 

 

 

Other repositories

Real-time optimisation employing Gaussian processes: https://github.com/Eric-Bradford/GP-MA

This repository is an example of the methods described in Modifier-Adaptation Schemes Employing Gaussian Processes and Trust Regions for Real-Time Optimization

 

 

 

 

 

 

 

 

 

 

Gaussian process, Bayesian optimisation, dynamic systems

Google Collab notebook tutorial on Gaussian process regression

Google Collab notebook tutorial on Bayesian optimisation using Gaussian processes

Google Collab notebook tutorial on Dynamic systems and uncertainty propagation