AI-Driven Automated Alerting and Root Cause Analysis for Optimized CCUS Value Chain Operations

Abstract Carbon Capture, Utilization, and Storage (CCUS) processes involve multiple interdependent stages, from capturing CO2 emissions to transporting, utilizing, and permanently storing them. These operations are complex, dynamic, and sensitive to fluctuations, requiring robust monitoring systems to ensure efficiency, safety, and regulatory compliance. However, current monitoring frameworks typically rely on static thresholds and manually configured rules, which are inadequate for managing the variable conditions in CCUS environments. These legacy systems often generate excessive false alarms, fail to detect anomalies in real time, and delay response actions. Moreover, identifying the root causes of process deviations remains time-consuming and inconsistent, hindering operational effectiveness and regulatory adherence. There is a critical need for an intelligent, adaptive system that can monitor CCUS processes holistically, detect anomalies dynamically, and recommend corrective actions without relying on static configurations. We propose an AI-driven alerting and Root Cause Analysis (RCA) system tailored for CCUS operations. This solution uses a combination of unsupervised and reinforcement learning techniques to continuously analyze data across all stages of the CCUS value chain. Unsupervised models identify deviations from expected behavior without requiring labelled datasets, while reinforcement learning enables the system to improve detection accuracy over time through interaction with users and system feedback. A domain-specific knowledge graph captures relationships between historical incidents, enabling automated RCA and the suggestion of resolution strategies. The system integrates seamlessly with industrial IoT devices, SCADA systems, and cloud-based data platforms, creating a unified, real-time monitoring environment. The AI-driven system automates anomaly detection and RCA, removing the need for static, rule-based alerting. It categorizes alerts by severity and contextualizes them using historical data, operational standards, and regulatory criteria. When an alert is triggered, the system offers actionable insights and suggests resolution pathways, improving both speed and quality of decision-making. A feedback loop further enhances system accuracy and adaptability. Ultimately, this solution improves operational efficiency, reduces downtime, ensures environmental compliance, and minimizes the risk of human error. It empowers engineers and operators with a visual interface that delivers alerts, analytics, and recommendations in real time. The novelty of this approach lies in its self-learning capabilities and its integration of machine learning with domain-specific knowledge. Unlike traditional systems, it continuously adapts to changing operational patterns, eliminating the need for manual reconfiguration. The use of a knowledge graph for RCA and the integration of reinforcement learning distinguish this solution as a scalable, intelligent framework for sustainable CCUS operations.