Abstract
A key technology for integrating fluctuating renewable energy into the process industry is the production of green hydrogen through water electrolysis plants. Scaling up electrolysis plant capacity remains a significant challenge for the renewable energy transition. System simulation of large-scale electrolysis plants can support process design, monitoring, optimization, and maintenance scheduling. Hybrid modeling methods are promising for improving simulation reliability by combining process knowledge with process data, addressing gaps in understanding of the underlying processes. These hybrid, semi-parametric models have shown improved accuracy than purely mechanistic models. This study develops a hybrid, semi-parametric model for an anion exchange membrane electrolysis (AEMEL) stack unit. Parameters such as heat loss and heat transfer, which cannot be directly measured, are estimated using real process data. Sensors provide data on lye tank temperature, outlet temperature, and flow rate, enabling estimation of heat transfer coefficients and losses. The hybrid model is validated against operational data from different load settings of the AEMEL stack unit. To test its scalability, a large-scale electrolysis plant configuration is simulated, comprising multiple AEMEL stack units, a water supply module. Performance accuracy and efficiency of the hybrid model are compared with the mechanistic model. This hybrid model lays the foundation for future use in efficient system simulations with surrogate models, potentially enhancing large-scale electrolysis plant performance and renewable energy integration.