In today’s highly dynamic and risk-sensitive industrial landscape, AI-augmented digitalization is transforming how process safety is monitored, governed, and improved. One of the persistent challenges in integrated energy organizations operating across upstream, midstream, downstream, and retail segments is the fragmentation of alarm management and process safety systems. This paper presents a practical and scalable digital platform that unifies alarm performance monitoring, protection system integrity tracking, and Major Accident Hazard (MAH) risk visualization, laying the groundwork for AI-driven operational intelligence across a complex enterprise.
At its core, the platform consolidates EEMUA 191 alarm performance metrics from diverse assets—including onshore, offshore, gas processing, refining, petrochemical, and retail facilities—into a unified analytics engine. By ingesting and harmonizing data from Distributed Control Systems (DCS), Safety Instrumented Systems (SIS), and alarm historians, it enables benchmarking, operator workload tracking, and systemic issue identification. AI-enabled analytics provide insights into alarm floods, stale alarms, chattering, and response degradation across facilities, delivering high-level visibility to both operations and leadership.
A key innovation is the automated tracking of alarm attribute changes—such as source tags, types, and priorities—which supports compliance with rationalization rules and detects unauthorized modifications. Advanced pattern recognition algorithms monitor deviations from safe operating limits (SOLs), predicting shifts in behavior and enabling proactive intervention before deviations escalate into incidents. This is critical in supporting real-time risk governance and reducing lag in operational response.
The platform extends beyond alarm analytics to provide continuous health validation of protection systems, aggregating proof test results, diagnostic data, bypass records, and failure reports. A live integrity scoring model evaluates the availability of protection components—sensors, logic solvers, and final elements—against their design intent, improving transparency and decision-making during maintenance, testing, or abnormal operations. These insights are connected to MAH scenarios via live bowtie models that update the risk landscape based on the actual health and availability of preventive and mitigative barriers.
When deviations or system activations occur, the platform automatically triggers a structured root cause analysis (RCA) workflow to capture causes, learning, and actions. RCA outcomes are stored in a centralized knowledge base, supporting organizational learning and cross-site improvement. The system further contextualizes incident analysis using digital twins and process simulations for deeper learning.
A central feature is the enterprise-wide process safety repository, which stores dynamic hazard analysis data, barrier status, and Safety Requirement Specifications (SRS). By integrating this repository with real-time monitoring, the platform becomes a living digital model of process risk, reflecting true operating conditions rather than static design assumptions. It also enables harmonization of protection strategies across similar facilities and streamlines audit readiness.
The system is modular, cloud-enabled, and integrates seamlessly with legacy and modern architectures—delivering scalable, secure, and high-value digitalization with minimal disruption. Through machine learning, the platform continues to evolve by recognizing leading indicators of abnormal trends, operator fatigue, or system reliability deterioration—enabling a transition from reactive to predictive safety governance.
In summary, this paper highlights a practical and impactful use case of AI and digital technologies in enhancing process safety. It demonstrates how the convergence of real-time data, intelligent analytics, and centralized safety intelligence creates a resilient, insightful, and adaptive safety infrastructure. The transformation from fragmented signal monitoring to AI-powered strategy supports not only compliance but also proactive, reliable, and profitable production across the energy value chain.