Predicting corrosion in LNG and ammonia infrastructure is fundamentally different from conventional oil & gas. The energy transition is introducing entirely new corrosion risks that traditional models were never designed to handle.
The Standards Gap. Traditional models such as API 1104 and NORSOK M-506 lack coverage for hydrogen, ammonia, and CCS environments. Their effectiveness drops from approximately 95% in conventional scenarios to below 50% in these new energy transition contexts. This is not a minor gap — it represents a fundamental blind spot in asset integrity management.
Physics Changes Everything. Corrosion variability increases dramatically in hydrogen and ammonia settings. While conventional oil & gas environments show a variability of around ±20%, hydrogen and ammonia environments exhibit variability of up to ±400%. This dramatic increase is driven by trace water, pressure cycling, stress corrosion cracking (SCC), and hydrogen embrittlement (HE).
Material Impact Is Severe. Carbon steel loses more than 50% of its tensile strength and more than 90% of its ductility under hydrogen exposure. These are not marginal effects — they fundamentally change how we must approach asset integrity in hydrogen infrastructure.
CorrosionAI: Physics-Informed AI for the Energy Transition. This is exactly why we built CorrosionAI — a physics-informed AI platform specifically designed for these new challenges. Our solution offers:
SCC and HE Prediction — detecting stress corrosion cracking and hydrogen embrittlement before they become critical failures.
H2S Detection at Trace Levels — identifying hydrogen sulfide concentrations from 10 to 10,000 ppm, a range where traditional models are essentially blind.
Remaining Useful Life Forecasts — providing 1 to 20 year RUL predictions with Bayesian confidence intervals, giving asset managers the information they need for long-term planning.
Real-Time Drift Monitoring — continuously tracking model performance and environmental changes to ensure predictions remain accurate over time.
Explainable AI — using SHAP and LIME to decompose every prediction into its contributing physical factors, so engineers understand not just what the model predicts, but why.
Performance Metrics. CorrosionAI achieves physics compliance above 99% and R² = 96.32%, validated against experimental datasets. These are not black-box predictions — they are grounded in electrochemical reality.
The energy transition demands new tools for new challenges. Traditional corrosion models served us well in conventional environments, but hydrogen, ammonia, LNG, and CCUS infrastructure require a fundamentally different approach. CorrosionAI delivers exactly that.