Technical Report - Corrosion Analysis
CorrosionAI-CO2 v2.1
TCR-20251111
Date
11 November 2025
Audience
Engineering
System
PI-GNN Architecture
Executive Summary
HIGH
Risk Level
70.0%
Probability
3.96
mm/year
6.1
Years Remaining
The analysis using Physics-Informed Graph Neural Networks (PI-GNN) indicates a HIGH risk level with a 70% corrosion probability. The estimated corrosion rate projects a remaining life of 6.1 years under current conditions.
Methodology
Computational Model
- Architecture: Physics-Informed Graph Neural Network
- Validation: Cross-validation k-fold (k=10)
- Uncertainty: Bayesian Monte Carlo Analysis
- Physics: Integrated Butler-Volmer and Pourbaix models
Data Quality
StatusDrift Detected
Drift Score0.172
Analysis Results
Uncertainty Analysis
Mean1.978 mm/year
Standard deviation0.844
95% Interval[0.740, 3.768]
Physical Validation
Butler-Volmer10.467 mm/year
PourbaixStable phase - Fe
MechanismModerate
Technical Recommendations
1
Review material specifications per NACE MR0175
2
Evaluate implementation of corrosion inhibitors
3
Optimize process parameters (pH, temperature)
4
Consider material upgrade in critical zones
Model Specifications
0.952
R² Score
0.087
RMSE
0.063
MAE
25K+
Training Data
Normative References
NACE MR0175/ISO 15156API 579-1/ASME FFS-1DNV-RP-F104Butler-VolmerPourbaix