Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation

• URL: http://arxiv.org/abs/2511.05879v1
• Date: Sat, 08 Nov 2025 06:41:39 GMT
• Title: Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation
• Abstract summary: Green hydrogen production via polymer electrolyte membrane (PEM) water electrolysis is pivotal for energy transition.<n>Current physics-based models require extensive calibration and computational resources that preclude real-time implementation.<n>Here we present the first application of physics-informed neural networks (PINNs) for hydrogen crossover prediction.
• Score: 2.3704813250344436
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Green hydrogen production via polymer electrolyte membrane (PEM) water electrolysis is pivotal for energy transition, yet hydrogen crossover through membranes threatens safety and economic viability-approaching explosive limits (4 mol% H$_2$ in O$_2$) while reducing Faradaic efficiency by 2.5%. Current physics-based models require extensive calibration and computational resources that preclude real-time implementation, while purely data-driven approaches fail to extrapolate beyond training conditions-critical for dynamic electrolyzer operation. Here we present the first application of physics-informed neural networks (PINNs) for hydrogen crossover prediction, integrating mass conservation, Fick's diffusion law, and Henry's solubility law within a compact architecture (17,793 parameters). Validated across six membranes under industrially relevant conditions (0.05-5.0 A/cm$^2$, 1-200 bar, 25-85{\deg}C), our PINN achieves exceptional accuracy (R$^2$ = 99.84%, RMSE = 0.0348%) with sub-millisecond inference times suitable for real-time control. Remarkably, the model maintains R$^2$ > 86% when predicting crossover at pressures 2.5x beyond training range-substantially outperforming pure neural networks (R$^2$ = 43.4%). The hardware-agnostic deployment, from desktop CPUs to edge devices (Raspberry Pi 4), enables distributed safety monitoring essential for gigawatt-scale installations. By bridging physical rigor and computational efficiency, this work establishes a new paradigm for real-time electrolyzer monitoring, accelerating deployment of safe, efficient green hydrogen infrastructure crucial for net-zero emissions targets.

Related papers

• Performance Monitoring of Proton Exchange Membrane Water Electrolyzer by Transformers-Based Machine Learning Model [6.608674167337045]
  Green hydrogen plays an essential role in decarbonization, with capacity projected to scale to 560 GW by 2030.<n>We present a machine learning framework that performs virtual electrochemical characterization during normal operation.<n>Inspired by patch-based sequence tokenization, we segment the inputs into patches and encode them to form meaningful tokens.
  arXiv  Detail & Related papers  (2026-05-18T20:49:56Z)
• Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing [0.0]
  Accurately predicting protein-ligand binding free energies (Bs) remains a central challenge in drug discovery.<n>Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial charges.<n>Across 23 protein targets and 543, the method achieves a mean absolute error of about 1.10 kcal/mol with strong rank-order fidelity.
  arXiv  Detail & Related papers  (2025-12-05T20:47:34Z)
• Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations [0.0]
  The research introduces a machine learning-based three-dimensional hybrid fluid-particle-in-cell (PIC) system.<n>The technique employs fluid approximations for stable areas but activates the PIC solver when SwitchNet directs it to unstable sections.<n>The model produces precise predictions with coefficient of determination (R2) values above 0.95 and mean squared errors below 10-4 for all field components.
  arXiv  Detail & Related papers  (2025-10-13T09:07:59Z)
• Adaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals [1.5749416770494706]
  This study introduces an adaptive physics-informed neural network (PINN) framework to enhance hydrogen sorption prediction.<n>The PINN employs deep residual networks with multi-head attention, optimized via adaptive loss functions and Monte Carlo dropout for uncertainty quantification.<n>The framework demonstrates robust lithology-specific performance across clay minerals, shales, and coals, maintaining 85-91% reliability scores.
  arXiv  Detail & Related papers  (2025-08-24T19:41:33Z)
• Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation [1.099532646524593]
  We benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO)<n>DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.
  arXiv  Detail & Related papers  (2025-08-11T15:31:23Z)
• Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks [45.32169712547367]
  Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production.<n>Their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges.<n>Traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate.<n>This study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers.
  arXiv  Detail & Related papers  (2025-06-19T15:46:49Z)
• Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems [52.19558333652367]
  We present finite-range embeddings (FiRE) for accurate large-scale ab-initio electronic structure calculations.<n>FiRE reduces the complexity of neural-network variational Monte Carlo (NN-VMC) by $sim ntextel$, the number of electrons.<n>We validate our method's accuracy on various challenging systems, including biochemical compounds and organometallic compounds.
  arXiv  Detail & Related papers  (2025-04-08T14:28:54Z)
• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation.<n>This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs [5.759388420139191]
  We propose a novel method to automatically predict Coulombic Efficiency (CE) based on the composition of liquid electrolytes. We input the obtained electrolyte features into a Multi-layer Perceptron or Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world dataset demonstrate that our method achieves SOTA for predicting CE compared to all baselines.
  arXiv  Detail & Related papers  (2024-07-24T14:45:25Z)
• Faster spectral density calculation using energy moments [77.34726150561087]
  We reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian. One of the main advantages of this framework is that it allows for an important reduction of the computational cost.
  arXiv  Detail & Related papers  (2022-11-01T23:57:58Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow [49.2540510330407]
  AC-OPF is a key building block in many power system applications. Motivated by increased penetration of renewable sources, this paper explores deep learning to deliver efficient approximations to the AC-OPF.
  arXiv  Detail & Related papers  (2020-06-29T20:22:16Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.