Description

The motivation of this thesis will be to research and compare classical control strate- gies and novel control approaches applied to alkaline electrolyzers, and also to develop hybrid controllers considering concepts of optimal, artificial intelligence, predictive, and sliding mode techniques. In this way, this project will contribute to the development of technologies to optimize the purity of hydrogen when these control techniques are applied to regulate the pressure and levels of electrolyzer tanks.

The work is under the scope of the following projects:

• L-BEST: Supervision and fault-tolerant control of smart infrastructures based on advanced learning and optimization (web)
• MASHED: Efficient Management of Energy Systems including Hybrid Electrochemical Energy Storage using Digitalisation Technologies (web)