Scope:
This workshop introduces Model Predictive Control (MPC) using the MPCcode software, covering both theoretical concepts and practical implementation. Participants will review key MPC principles, including predictive modeling, constraint handling, and optimization, and apply them to selected case studies. The focus will be on chemical engineering applications, though the methodology extends naturally to broader industrial automation problems. Through guided exercises, attendees will learn how to use MPCcode to modify controller architectures and internal models with simple yet useful options. Prior knowledge of MPC and Python is helpful but not required.
Pre-workshop Instructions:
Please navigate to the GitHub repository and follow the instructions in the README.md file to prepare your container. The repository includes a Dev Container (let’s say a “magic box”), a flexible container as our development environment, where we can run all files, to avoid local installations, with possible issues with versions, compatibilities, and dependencies. (Many thanks to Marek Wadinger for the great support.) Do not hesitate to contact for any doubts or issues.