Artificial Intelligence and Machine Learning Applications in Hydrogen-blend Natural Gas Pipeline Transportation

Hydrogen blending into natural gas pipelines is a promising strategy for decarbonizing energy systems while utilizing existing infrastructure. Current global research and industrial standards recommend blending ratios of 5–20% by volume, as this range balances meaningful emission reductions with material compatibility constraints of existing pipelines and end-use appliances. A blending ratio of up to 20% is adopted as the research boundary in this study, representing the upper threshold endorsed by regulatory bodies and pilot programs worldwide and providing specific physical boundary conditions for discussing challenges such as hydrogen diffusion behavior, explosion risk thresholds, and compressor efficiency decline. However, hydrogen blending introduces technical challenges, including material embrittlement, increased leakage risk, and reduced operational efficiency. This review examines how Artificial Intelligence (AI) and Machine Learning (ML) can address these challenges in hydrogen-blended natural gas pipeline systems. Predictive analytics, physics-informed ML, and digital twins improve flow modeling, material behavior prediction, and fault detection. AI-controlled optimization manages hydrogen injection, compressor control, and blending ratios to maintain a consistent energy supply. AI-enabled sensors detect anomalies in real time, accounting for hydrogen's flammability and high diffusivity, thereby enhancing safety. Supply chain efficiency, cost reduction, and emissions minimization are further supported through AIdriven real-time process management. Despite these advances, challenges in data availability, model generalisation, cybersecurity, and interpretability remain significant barriers. Overcoming them requires interdisciplinary collaboration and the development of Explainable AI (XAI) and edge computing frameworks. This review demonstrates that AI can accelerate the transition to safe, dependable, and affordable hydrogen energy systems.