Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production, yet their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges. Therefore, accurate modeling of this degradation is essential for optimizing durability and performance. To address these concerns, traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate. Conversely, data-driven approaches, such as machine learning, offer flexibility but may lack physical consistency and generalizability. To address these limitations, this study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers. The proposed PINN framework couples two ordinary differential equations, one modeling membrane thinning via a first-order degradation law and another governing the time evolution of the cell voltage under membrane degradation. Results demonstrate that the PINN accurately captures the long-term system's degradation dynamics while preserving physical interpretability with limited noisy data. Consequently, this work introduces a novel hybrid modeling approach for estimating and understanding membrane degradation mechanisms in PEM electrolyzers, offering a foundation for more robust predictive tools in electrochemical system diagnostics.

Power-to-Hydrogen is crucial for the renewable energy transition, yet existing literature lacks business models for the significant excess heat it generates. This study addresses this by evaluating three models for selling electrolyzer-generated heat to district heating grids: constant, flexible, and renewable-source hydrogen production, with and without heat sales. Using agent-based modeling and multi-criteria decision-making methods (VIKOR, TOPSIS, PROMETHEE), it finds that selling excess heat can cut hydrogen production costs by 5.6%. The optimal model operates flexibly with electricity spot prices, includes heat sales, and maintains a hydrogen price of 3.3 EUR/kg. Environmentally, hydrogen production from grid electricity could emit up to 13,783.8 tons of CO2 over four years from 2023. The best economic and environmental model uses renewable sources and sells heat at 3.5 EUR/kg

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.

The integration of Machine Learning (ML) with domain-specific knowledge is a pivotal advancement in predictive modeling [1, 2]. This combination has brought a new level of precision and insight to fields within engineering and environmental sciences [3, 4]. While the synergy has notably improved accuracy and decision-making processes [5, 6], the challenge of seamlessly blending domain knowledge with ML algorithms continues to evolve. To bridge this gap, the Ladder of Knowledge-integrated Machine Learning has been introduced [7]. This framework aims to optimize the utilization of domain-specific insights, offering a comprehensive approach to integrating prior knowledge information into ML applications. Inspired by the long debate between holistic and reductionist approaches in ML [8], the framework aims firstly to synergize multidisciplinary domain knowledge with data-driven processes in two principal dimensions: firstly, by identifying and understanding the complementary nature of uncertainties in data, knowledge-based methodologies, and data-driven methods; secondly, by exploring knowledge decomposition from various perspectives and aligning these insights with our paradigm. Finally, building upon the previous two foundations in the specific domain context, the ladder unfolds across three progressive levels of integrating domain expertise into ML approaches [7]. In the pursuit of sustainable energy solutions, Proton Exchange Membrane Water Electrolyzers (PEMWEs) stand out for their high energy efficiency and minimal environmental impact [9] in hydrogen production.

It is a sparkling, sunny August morning in 2043, as your Air China flight from Beijing touches down gracefully (and almost silently) at LAX. The sleek plane is one of a new generation of hydrogen-powered wide-body jets manufactured by Commercial Aircraft Corp. of China -- the kind of innovation that helped the state-owned company sail past Boeing and Airbus in the 2030s to become the world's largest aerospace group. Starting with the Inflation Reduction Act in 2022, the last two decades have seen massive efforts to clean up transportation all around the United States and throughout the world. Back in the early 2020s, transportation accounted for 29% of America's greenhouse gas emissions, but that number has been steadily dwindling to almost zero -- resulting in cleaner cities everywhere. Not only have electric and hydrogen-powered vehicles replaced gas-guzzling cars, but many people have forsaken car-ownership altogether, in favor of much more economic and widely available solutions like e-bikes, robo-taxis and public transit.

Predicting faults before they occur helps to avoid potential safety hazards. Furthermore, planning the required maintenance actions in advance reduces operation costs. In this article, the focus is on electrochemical cells. In order to predict a cell's fault, the typical approach is to estimate the expected voltage that a healthy cell would present and compare it with the cell's measured voltage in real-time. This approach is possible because, when a fault is about to happen, the cell's measured voltage differs from the one expected for the same operating conditions. However, estimating the expected voltage is challenging, as the voltage of a healthy cell is also affected by its degradation -- an unknown parameter. Expert-defined parametric models are currently used for this estimation task. Instead, we propose the use of a neural network model based on an encoder-decoder architecture. The network receives the operating conditions as input. The encoder's task is to find a faithful representation of the cell's degradation and to pass it to the decoder, which in turn predicts the expected cell's voltage. As no labeled degradation data is given to the network, we consider our approach to be a self-supervised encoder. Results show that we were able to predict the voltage of multiple cells while diminishing the prediction error that was obtained by the parametric models by 53%. This improvement enabled our network to predict a fault 31 hours before it happened, a 64% increase in reaction time compared to the parametric model. Moreover, the output of the encoder can be plotted, adding interpretability to the neural network model.

IMAGE: Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part... view more Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources.

After 18 months living and working on the surface of Mars, a crew of six explorers boards a deep-space transport rocket and leaves for Earth. No humans are staying behind, but work goes on without them: Autonomous robots will keep running a mining and chemical-synthesis plant they'd started years before this first crewed mission ever set foot on the planet. The plant produces water, oxygen, and rocket fuel using local resources, and it will methodically build up all the necessary supplies for the next Mars mission, set to arrive in another two years. This robot factory isn't science fiction: It's being developed jointly by multiple teams across NASA. One of them is the Swamp Works Lab at NASA's John F. Kennedy Space Center, in Florida, where I am a team lead. Officially, it's known as an in situ resource utilization (ISRU) system, but we like to call it a dust-to-thrust factory, because it turns simple dust into rocket fuel.

Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicles in future taxi industry. The cost of hydrogen production is minimized by utilizing the proposed forecasting tool to reduce the hydrogen produced during high cost on-peak hours and guide hydrogen producer to store sufficient hydrogen during low cost off-peak hours.