AI-Based Prediction-Driven Control Framework for Hydrogen–Natural Gas Blends in Natural Gas Networks

George Calianu, Ștefan-Ionuț Spiridon, Andrei-Catalin Militaru, Antoaneta Roman, Marius Constantinescu, Felicia Bucura, Roxana Elena Ionete and Eusebiu Ilarian Ionete ()
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George Calianu: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Ștefan-Ionuț Spiridon: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Andrei-Catalin Militaru: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Antoaneta Roman: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Marius Constantinescu: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Felicia Bucura: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Roxana Elena Ionete: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania
Eusebiu Ilarian Ionete: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Râmnicu Vâlcea, 4th Uzinei Street, 240050 Râmnicu Vâlcea, Romania

Energies, 2025, vol. 18, issue 18, 1-22

Abstract: This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm 3 and a coefficient of determination (R 2 ) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures.

Keywords: hydrogen; blending; Artificial Intelligence; prediction; combustion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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