H2 Purity-Aware Model-free Predictive Control of High-Pressure Alkaline Electrolyzers

Abstract

This paper introduces a Model-free Predictive Control (MFPC) scheme to optimize the hydrogen purity output in a nonlinear high-pressure alkaline electrolyzer. In contrast to conventional Model Predictive Control (MPC), the proposed MFPC approach eliminates the need for an explicit system model by employing the Model-free approach (an ultra-local model combined with real-time optimization). The controller adaptively compensates the electrolyzer for variations in electric current as well as pressurization and depressurization phases, while ensuring compliance with operational constraints. In four different operation scenarios, this study shows that MFPC exceeds standard Model-free Controllers (MFC), providing improved performance indicators such as reduced gas impurities, smoother control signals, and lower RMSE, ISE, IAE, and ITAE indices. Furthermore, similar to conventional MPC controllers, MFPC prevents constraint violations and also actuator overload, commonly encountered in MFC and MPC-based schemes, demonstrating its suitability for managing complex nonlinear processes.