Causal Explainable Artificial Intelligence for Predictive Safety Analytics and Resilient Autonomous Control in Safety-Critical Process Industries

Safety-critical process industries including oil and gas, nuclear power generation, chemical manufacturing, and pharmaceutical production face mounting pressure to adopt intelligent monitoring and autonomous control systems capable of preventing catastrophic failures while maintaining operational efficiency. Conventional machine learning (ML) approaches, though predictively powerful, suffer from opacity, spurious correlations, and brittleness under distributional shift, rendering them unsuitable for high-stakes deployment without interpretable assurance mechanisms. This paper proposes a unified framework integrating Causal Explainable Artificial Intelligence (Causal XAI) with predictive safety analytics and resilient autonomous control architectures. By embedding structural causal models (SCMs) within deep learning pipelines and augmenting them with counterfactual reasoning and do-calculus interventions, the framework enables causally grounded anomaly detection, root-cause attribution, and intervention recommendation. We demonstrate the framework's efficacy through case studies across a refinery distillation unit, a nuclear reactor coolant loop, and a pharmaceutical batch reactor. Results indicate a ۳۴.۷% improvement in early fault detection lead time, a ۲۸.۱% reduction in false positive safety alerts, and a ۴۱.۳% improvement in root-cause identification accuracy compared to state-of-the-art correlation-based XAI methods. The proposed architecture advances the conceptual and practical foundations for safety-aware, causally transparent autonomous systems in high-consequence industrial environments.

کلیدواژه ها:

Causal AI ، Explainable AI ، Process Safety ، Autonomous Control ، Structural Causal Models ، Predictive Safety Analytics ، Industrial Fault Detection ، Do-calculus ، Counterfactual Reasoning