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Article

Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor

1
CNNP Rich Energy Corporation Limited, Beijing 100071, China
2
School of Chemical Engineering, Sichuan University, Chengdu 610065, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Processes 2025, 13(8), 2457; https://doi.org/10.3390/pr13082457 (registering DOI)
Submission received: 9 June 2025 / Revised: 24 July 2025 / Accepted: 1 August 2025 / Published: 3 August 2025
(This article belongs to the Section AI-Enabled Process Engineering)

Abstract

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k0 and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization.
Keywords: physics-informed neural networks; computational fluid dynamics; ammonia synthesis reactor physics-informed neural networks; computational fluid dynamics; ammonia synthesis reactor

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MDPI and ACS Style

Xu, R.; Zhang, S.; Rong, F.; Fan, W.; Zhang, X.; Wang, Y.; Zan, L.; Ji, X.; He, G. Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor. Processes 2025, 13, 2457. https://doi.org/10.3390/pr13082457

AMA Style

Xu R, Zhang S, Rong F, Fan W, Zhang X, Wang Y, Zan L, Ji X, He G. Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor. Processes. 2025; 13(8):2457. https://doi.org/10.3390/pr13082457

Chicago/Turabian Style

Xu, Ran, Shibin Zhang, Fengwei Rong, Wei Fan, Xiaomeng Zhang, Yunlong Wang, Liang Zan, Xu Ji, and Ge He. 2025. "Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor" Processes 13, no. 8: 2457. https://doi.org/10.3390/pr13082457

APA Style

Xu, R., Zhang, S., Rong, F., Fan, W., Zhang, X., Wang, Y., Zan, L., Ji, X., & He, G. (2025). Physics-Informed Neural Network Enhanced CFD Simulation of Two-Dimensional Green Ammonia Synthesis Reactor. Processes, 13(8), 2457. https://doi.org/10.3390/pr13082457

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