Seawater Hydrogen Production: A Blue Ocean Strategy for Green Energy Guided by Intelligent Technology and Predictive Modeling

Dear Colleagues,

In the global pursuit of clean and sustainable energy to address climate change and achieve net-zero emission targets, hydrogen energy, with its high energy density and zero-carbon emission characteristics, is regarded as a cornerstone of the future energy portfolio. Conventional water electrolysis for hydrogen production is heavily reliant on precious freshwater resources. However, given that over 96.5% of the Earth's water is seawater, direct hydrogen production from seawater emerges as a highly attractive and promising frontier. This Special Issue aims to consolidate the latest research findings and breakthroughs in the field of seawater hydrogen production, with a particular focus on how Artificial Intelligence (AI), big data analytics, and advanced predictive models are accelerating technological innovation. We seek to foster an in-depth exploration of the multifaceted challenges and opportunities, from fundamental science to practical applications.

Direct seawater electrolysis confronts several formidable challenges. The complex ionic composition of seawater, especially the high concentration of chloride ions, triggers a parasitic chlorine evolution reaction (CER) at the anode. This not only generates toxic chlorine gas but also severely corrodes the electrode materials, drastically reducing the lifespan and stability of the electrolyzer. Furthermore, ions such as calcium and magnesium can precipitate as hydroxides near the cathode, blocking active sites and diminishing hydrogen production efficiency.

To surmount these obstacles, researchers worldwide are actively developing innovative solutions wherein AI and big data analytics have demonstrated transformative potential.

This Special Issue will have a primary focus on the following cutting-edge research directions:

1. AI- and Predictive Model-Driven Development of High-Performance Electrocatalysts: Leveraging machine learning to construct predictive models for analyzing vast materials science databases can significantly accelerate the screening and discovery of novel electrocatalysts with superior activity, selectivity, and stability. Before actual synthesis and testing, these models can efficiently predict material properties, rapidly identifying promising catalyst compositions that effectively suppress the CER while enhancing the Oxygen Evolution Reaction (OER), thereby guiding rational experimental design.
2. Intelligent Electrolyzer Design and Process Optimization: The development of digital twins and dynamic predictive models for electrolyzers allows for the simulation and prediction of complex electrochemical reactions, fluid dynamics, and mass/heat transfer phenomena based on real-time sensor data. This enables proactive and intelligent control over critical parameters such as temperature, pressure, and voltage, facilitating the real-time suppression of side reactions and maximizing hydrogen production efficiency at minimal energy cost.
3. Big Data Analytics for Predictive Maintenance: By establishing predictive models for the performance degradation of critical components (e.g., electrodes, membranes) using historical operational data, AI can identify early-warning signs of failure from subtle data fluctuations. This enables the accurate prediction of the Remaining Useful Life (RUL) of equipment, facilitating a paradigm shift from reactive to predictive maintenance, which significantly enhances system reliability and economic viability.
4. Smart Integration with Renewable Energy Sources: Utilizing big data and advanced time-series forecasting models, AI can accurately predict the intermittent supply from renewable energy sources, such as offshore wind and solar power. Based on these forecasts, intelligent scheduling systems can proactively plan the start-up, shutdown, and load-following operations of seawater electrolysis plants, ensuring maximal utilization of green electricity for the stable, large-scale production of low-cost green hydrogen.
5. Autonomous Seawater Hydrogen Production Systems and Resource Valorization: This area explores novel concepts, such as "energy sailing vessels," which combine AI algorithms, path-planning predictive models, and autonomous navigation to independently seek optimal wind fields for mobile seawater-to-hydrogen conversion. Concurrently, AI models can optimize the process of extracting high-value by-products, such as lithium, from concentrated brine, predicting optimal extraction timings and conditions to achieve the dual benefits of energy production and resource recovery.

We cordially invite scientists and engineers from around the globe to submit original research articles and comprehensive review papers addressing the critical scientific questions and technological bottlenecks in seawater hydrogen production outlined above. Through this Special Issue, we aim to foster academic exchange, inspire innovative thinking, and collectively advance the development of AI- and predictive model-empowered seawater hydrogen technology, thereby making a significant contribution to the global energy transition and sustainable development.

Prof. Dr. Horng-Jinh Chang
Guest Editor

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• seawater electrolysis
• hydrogen production
• green hydrogen
• artificial intelligence
• big data
• predictive modeling
• machine learning
• electrocatalysis
• process optimization
• predictive maintenance