Machine Learning-Based Predictive Pressure Control in Electrolysis Systems

Abstract

This thesis proposes a predictive maintenance model for pressure control systems in electrolysis plants, utilizing advanced machine learning algorithms to enhance the reliability, safety, and efficiency of green hydrogen production. Electrolysis, a cornerstone of green hydrogen production, is often hindered by pressure control component failures, resulting in production losses and safety hazards. Traditional reactive or scheduled maintenance approaches are costly and lead to downtime, further exacerbating inefficiencies. To address these challenges, the proposed framework leverages multivariate operational data and sophisticated machine learning algorithms to proactively predict failures and prevent breakdowns from occurring. Some of the key challenges addressed include the complexities of processing data streams, selecting relevant features, rendering models robust, reducing false alarms, and ensuring simple industrial integration. Moreover, the system incorporates a Proportional-Integral-Derivative (PID) control loop, which is tuned by a Genetic Algorithm for dynamic temperature control of the electrolyzer to maintain optimum operating conditions. The system's efficiency in pressure and temperature stabilization is confirmed by experimental results, resulting in notable operating cost savings, enhanced safety features, and groundbreaking green hydrogen technology. Along with operational improvements, this research lays the groundwork for future studies to optimize maintenance approaches further and enhance the sustainability of hydrogen production. The introduction of machine learning into predictive maintenance not only transforms electrolysis plant operations but also paves the way for the broader adoption of green hydrogen technologies, which are critical to environmental sustainability and energy independence worldwide.