The design of chemical processes and their optimization is often performed with the aid of detailed computer simulations. These are accurate but very time-consuming. In this Ph.D. research line, we are absorbing the new advances in machine learning algorithms which are showing great success in fast predictive capabilities (convolutional neural networks, diffusion models, et c.) and using them in synergy with classical chemical engineering modelling research, to obtain faster and accurate design automated optimization workflows.
