<![CDATA[Industrial accidents in chemical process engineering continue to pose a significant issue despite the widespread use of Industry 4.0 technology and data-driven monitoring systems. Traditional safety frameworks often depend on either purely empirical machine learning models or deterministic first-principles simulations, creating a methodological split that constrains prediction reliability in uncommon, high-impact situations. This work bridges the structural gap by incorporating physics-informed artificial intelligence into a digital twin architecture for the avoidance of industrial accidents. A methodological framework driven by simulation was established, integrating first-principles process modeling, synthetic data generation with controlled fault injection, supervised and unsupervised learning, and reinforcement learning for safety-constrained optimization. Physics-based limitations were included into the learning aim to maintain thermodynamic and transport consistency. The model's performance was assessed using safety-oriented criteria, such as detection delay, false negative rate, resilience to sensor noise, and stability amid parametric uncertainty. Results demonstrate that physics-informed models significantly reduce detection latency and false negatives in accident precursor regimes compared to purely data-driven baselines. The integration of constraint-aware learning improves extrapolation stability under class imbalance conditions typical of industrial safety datasets. Furthermore, explainable AI mechanisms enhance interpretability and regulatory transparency. These findings indicate that AI-enhanced simulation models reconfigure accident prevention strategies by shifting from reactive threshold systems to proactive, mechanism-consistent risk anticipation frameworks applicable to safety-critical chemical processes.]]>
