Zhuang Kang

Recent activities

• Our review paper, “A Review of Model-Based Optimization for Hydrogen Refueling Stations,” was published by Springer in June 2026. Link.
• I present our work 'Simulation of AEM electroluzer with OpenFOAM and optimization' at WCCM ECCOMAS 2026, Munich.
• My supervised master thesis, "Energy Management for Hydrogen Refueling Stations: A Synergistic Approach of Cascade Compression Architecture and Deep Reinforcement Learning" by Dunxiang Lu, has been accepted by International Journal of Hydrogen Energy.

Introduction

I am Zhuang Kang (康庄), a PhD candidate in computer science at Leiden University, supervised by Yingjie Fan and Thomas Bäck. I also work with Michael Emmerich from University of Jyväskylä.

I work on HyPro, a Dutch hydrogen research project, where my delieverable is 'a digital twin of electrolyzer'. My current plan is developing a high-fidelity simulation model of electrolyzers and then using multi-objective optimization to find its optimal design and control parameters.

My research focuses on:

1. simulation and black-box optimization of electrolyzers.
2. optimal control of hydrogen energy systems.

I have a background in mathematics and now work at the intersection of modeling and optimization of energy systems. Before joining Leiden Univerisity, I studied in Sichuan Univerisity and Univerisity of Münster for my bachelor and master, both in mathematics. Besides study, I also worked at ABB AG(Mannheim) Optimization&Control group where my focus was simulation and optimal control of energy systems.

Publications

1. Zhuang Kang, Jacob de Nobel, Michael Emmerich, Thomas Bäck, and Yingjie Fan. A Review of Model-Based Optimization for Hydrogen Refueling Stations. Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control, Springer, 2026. [link]

Master’s students and supervision

Completed master’s theses

• Dunxiang Lu: Energy Management for Hydrogen Refueling Stations: A Synergistic Approach of Cascade Compression Architecture and Deep Reinforcement Learning
• Di Xie: Deep Reinforcement Learning vs. Engineering Rules for Hybrid EV/FCEV Charging Hub Scheduling with Cascade Hydrogen Storage

Current master’s students

• Dongjie Chen: Incremental Techno-Economic Analysis of RL-Controlled Cascade Compression-Storage in Hydrogen Refueling Stations

Prospective students

I welcome students interested in:

1. optimal control of energy systems
2. multiphysics simulation with OpenFOAM.
3. Black-box optimization, either theoretical or practical aspects.

To get in touch, send a short email with your name, and preferred topic listed above. I will reply and arrange a meeting.

Students have considerable freedom to develop their own research direction. For example, regarding the optimal control of energy systems, reinforcement learning, model predictive control and lots of other methods can be explored. The choice of method depends on students' expertise and interest.

I value open discussion about research goals. If you have concern regarding timelines, tuition fees and other practical matters, just talk to me.