Proactive Risk Mitigation and Asset Integrity via Physics-Informed Responsible AI: A Validated Framework for Hydrogen Production and Petrochemical Applications

Process safety in high-consequence industries—including petrochemical manufacturing and hydrogen production via Steam Methane Reforming (SMR)—is fundamentally challenged by complex nonlinear dynamics that render traditional reactive safety approaches inadequate for predicting emergent operational risks. This paper presents a rigorous, validated framework that integrates Physics-Informed Neural Networks (PINNs) with Bayesian Uncertainty Quantification (UQ) to enable proactive, predictive risk assessment with quantifiable confidence bounds. The core methodological innovation is a composite loss function that simultaneously enforces data fidelity and adherence to governing partial differential equations (PDEs) of reaction kinetics and heat transfer, ensuring physically consistent, extrapolation-capable predictions even under sparse data regimes. Crucially, the framework distinguishes between epistemic uncertainty (model ignorance) and aleatoric uncertainty (irreducible sensor noise), enabling operators to differentiate known operational variability from novel, potentially dangerous deviations. Quantitative validation is achieved through a deterministic, fully reproducible Python simulation (fixed random seed, PyTorch) of a 1000-hour SMR catalyst degradation scenario. The PINN-UQ framework achieves a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.98, compared to 0.84 for traditional Statistical Process Control (SPC/CUSUM), and reduces anomaly detection latency from 18 hours to 4 hours—a 78% reduction. Furthermore, the Remaining Useful Life (RUL) prediction Mean Absolute Error (MAE) is reduced from 25.5 to 5.2 days—an 80% improvement. The framework's applicability is further demonstrated for Fluid Catalytic Cracking (FCC) units (coke deposition kinetics) and Carbon Capture, Utilization, and Storage (CCUS) systems (amine solvent degradation; CO2 injection pressure monitoring), with a unified PDE-constrained Bayesian architecture applicable across all domains. The framework is aligned with Responsible AI (RAI) principles through explicit falsifiability criteria and human-in-the-loop decision support.