Hydrogen System Components

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction
Sep 28, 2026Md Abrar Rafid Siddique, Bibek Aryal, Qiugang LuHydrogen System Components Physics-Guided Diffusion
Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
Sep 15, 2026Niki Triantafyllou, Andrea Bernardi, Maria M. PapathanasiouMulti-Fidelity Multi-Objective Bayesian Optimization Algorithms
Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
Towards Sustainable Hydrogen Systems: Supply Chain Optimization with Model Predictive Control and Reinforcement Learning
Sep 14, 2026Mahammad ValiyevHydrogen System Components Model Predictive Control
Department of Chemical Engineering University of Southern California Los Angeles, California, United States
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Aug 13, 2026Žan Gorenc, Žiga Gradišar, Felix Mütter +2Slow Relaxation Modes Hydrogen System Components
Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, 1000, Slovenia · Institute of Thermal Engineering, Graz University of Technology, Inffeldgasse 25/B, Graz, 8010, Austria · Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia +1
Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach
Jul 17, 2026Wasimul Karim, Nur Mohammad Fahad, Abdul Hasib Siddique +4Hydrogen System Components Amodal Segmentation
Applied Artificial Intelligence and INtelligent Systems (AAIINS) Laboratory, Dhaka 1217, Bangladesh · School of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia · Department of Computer Science and Engineering, University of Scholars, 40 Kemal Ataturk Avenue, Dhaka 1213, Bangladesh +1
How Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection: A Safety Gap in Manual Leak Inspection of Hydrogen System Components
Jul 10, 2026Christian Masuhr, Arne Wendt, Thorsten SchüppstuhlMobile Gas Sensor Trajectories Govern Hydrogen Leak Detection Hydrogen System Components
Institute of Aircraft Production Technology (IFPT), Hamburg University of Technology (TUHH), Denickestraße 17, Hamburg, 21073, Germany
LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation
Jun 25, 2026Georgios Tsironis, Juan D. Medrano-Garcia, Gonzalo Guillen-GosalbezArtificial Intelligence Lifecycle Lifecycle
Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir Prelog Weg 1, Zürich 8093, Switzerland · NCCR Catalysis, Zurich 8093, Switzerland
NIMO: A Software Platform for Closed-Loop Materials Exploration with Diverse AI Algorithms
Jun 14, 2026Ryo Tamura, Naruki Yoshikawa, Koji Tsuda +1Data-Driven Materials Discovery Laboratory
Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan · Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan · Center for Green Research on Energy and Environmental Materials, National Institute for Material Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
Jun 12, 2026Shadi Heenatigala, Hasanika SamarasingheHydrogen System Components Geomagnetic Storms
Antioch College, Yellow Springs, OH, USA, 45387. · Department of Mechanical Engineering, The Open University of Sri Lanka, Nawala, Nugegoda, Sri Lanka.
Performance Monitoring of Proton Exchange Membrane Water Electrolyzer by Transformers-Based Machine Learning Model
May 18, 2026Bingqing Chen, Ivan Batalov, Qiu Chen +2Hydrogen System Components Transformer Encoder
Bosch Research & Technology Center
Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials
May 1, 2026Steven R. Spurgeon, Milad Abolhasani, Frederick Baddour +28Data-Driven Materials Discovery Hydrogen System Components
National Laboratory of the Rockies, Golden, CO, USA · Metallurgical and Materials Engineering Department, Colorado School of Mines, Golden, CO, USA · University of Colorado–Boulder, Boulder, CO, USA +10
Autonomous Reliability Qualification of Ga $_2$ O $_3$ -based Hydrogen and Temperature Sensors via Safe Active Learning
Apr 22, 2026Davi Febba, William A. Callahan, Anna Sacchi +1Hydrogen System Components Degradation
Materials Science Center, National Laboratory of the Rockies (NLR), Golden, CO 80401, USA
Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis
Nov 8, 2025Yong-Woon Kim, Jihyeok Lee, Chulung Kang +1Hydrogen System Components Polymer Property Prediction
Department of Computer Engineering, Jeju National University, Jeju, 63243, South Korea · Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea · Department of Mechanical System Engineering, Jeju National University, Jeju, 63243, South Korea +1

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Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction

Md Abrar Rafid Siddique, Bibek Aryal, Qiugang Lu

Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA

Hydrogen System Components Physics-Guided Diffusion Rare Events Diagnosis

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As the world moves towards sustainable energy sources, hydrogen (H2) can be treated as an eco-friendly alternative to fossil fuels due to its high energy density and zero carbon emissions. The water-gas shift (WGS) reaction is a widely used industrial process for hydrogen production by converting carbon monoxide and steam into hydrogen and carbon dioxide. However, occurrences like severe fouling, catalyst deterioration, and thermal runaway can hamper the reaction kinetics/process safety and decrease the yield of H2. These incidents are rare, and gathering process data under such abnormal conditions is challenging. In this work, we propose a physics-guided conditional diffusion model to generate realistic rare-event trajectories for the WGS reaction. The proposed model integrates a conditional denoising diffusion probabilistic model (CDDPM) with governing laws of the reaction to generate physically consistent process trajectories. The conditioning features allow the model to produce high-quality synthetic profiles for rare-event domains that are typically beyond the training regimes. The generated rare-event trajectories then augment the raw dataset for a balanced distribution between normal and abnormal conditions. We further propose a hazard score to assess the risk severity of the operating condition based on the operating trajectory. Deep learning models are trained with the augmented dataset to diagnose the health status of the reaction. Simulation results show that the proposed physics-guided diffusion model outperforms data-driven models in terms of the quality of synthetic data and diagnosis performance for rare events.

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