AI-Powered Condition Monitoring
AI-Powered Condition Monitoring for Safe and Sustainable Water Electrolysis
Safety and efficiency are essential for sustainable green hydrogen production. Helmut Lademann from R2 recently highlighted this at the H2 Tech Series during the Hydrogen Americas 2024 Summit in Washington. His presentation focused on R2’s unique AI-powered condition monitoring software for water electrolysis that tracks cell performance with ultimate precision to detect, in real-time, any abnormal performance declines for the earliest possible application of countermeasures.
Why AI-Powered Condition Monitoring is Essential for Water Electrolysis?
Bringing a water electrolyzer from startup to breakeven is a long journey – optimists estimate it takes 5 to 10 years. Yet, water electrolysis technology has not been tested at full scale under the unique load profiles of wind and solar power plants for such an extended period. The reality is that electrolyzer aging is irreversible, and refurbishment is both costly and time-consuming.
Investing in advanced condition monitoring is essential to ensuring long-term efficiency and reliability. AI-powered machine learning algorithms offer the most precise method for detecting abnormal performance declines at the earliest stages. By continuously modeling expected electrolyzer activity deviations from predicted performance can be identified enabling the earliest application of countermeasures. This proactive approach maximizes the return on investment in green hydrogen production, while minimizing downtime and maintenance expenses.
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Why AI-Powered Condition Monitoring per cell?
Water electrolyzers are bipolar systems, with cells connected in a series, aging at different times. As a chain, where the weakest link determines its strength, an electrolyzer’s lifespan is dictated by its most degraded cell.
R2’s EMOS platform redefines condition monitoring by going beyond traditional analysis. Instead of evaluating the overall electrolyzer stack, EMOS tracks and analyzes individual cell voltages to detect anomalies that may signal membrane degradation or other critical issues—long before they escalate. R2’s unique Early Detection Engine uses machine learning algorithms to model the behavior of each cell, forecast its end of life, and enable predictive maintenance for safe and sustainable operation.
Only AI-powered condition monitoring of individual cells can identify the weakest cell, access the impact of operating conditions, and suggest appropriate countermeasures.
R2’s Global Leadership in Electrolyzer Maintenance, Optimization and Safety
With more than 30 years of expertise and the worldwide largest database of monitoring over 100,000 cells from over 90 installations across 33 countries, R2 is the global leader in condition monitoring for electrolysis systems. Through the EMOS system, R2 provides not only patented hardware and software but also continuous analysis services for predictive maintenance. R2 distinguishes itself by collaborating closely with clients to ensure both safety and profitability in electrolyzer operation. Partnerships with industry leaders like Asahi Kasei underscore R2’s commitment to pioneering advances in electrolyzer technology.