Proactive Risk Mitigation and Asset Integrity via Physics-Informed Responsible Artificial Intelligence: 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, computationally 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 L(θ) = L_data + λ L_physics 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 decomposes total predictive uncertainty into epistemic (model ignorance, reducible) and aleatoric (sensor noise, irreducible) components, enabling operators to distinguish known operational variability from novel, potentially catastrophic deviations.Quantitative validation is achieved through a deterministic, fully reproducible Python simulation (NumPy and PyTorch; fixed seed 42) of a 1000-hour SMR catalyst degradation scenario benchmarked against traditional Statistical Process Control (SPC/CUSUM) and purely data-driven ML baselines.The PINN-UQ framework achieves a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.98, compared with 0.84 for SPC and 0.79 for a standard feed-forward neural network (traditional ML), and reduces anomaly detection latency from 18 hour to 4 hour—a 78% reduction.The false positive rate at 95% true positive rate falls from 12% (SPC) to 2% (PINN-UQ), directly mitigating operator alarm fatigue.Remaining Useful Life (RUL) prediction Mean Absolute Error (MAE) decreases from 25.5 days (SPC extrapolation) to 5.0 days (PINN-UQ)—an 80% improvement, enabling maintenance scheduling within a ±5.0 day window.The framework's domain-agnostic PDE substitution mechanism is 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).Responsible AI (RAI) compliance is ensured through explicit Popperian falsifiability criteria and human-in-the-loop decision support protocols.