Abstract : Carbon Capture, Utilization, and Storage (CCUS) is becoming an important part of global efforts to reduce carbon emissions. It is especially relevant for industries where emissions are difficult to avoid through electrification or renewable energy alone. Most CCUS research has focused on capture technology, CO₂ transport, and long-term storage. Less attention has been given to how equipment downtime affects the performance of the full CCUS value chain.

In practice, the amount of CO₂ that can be transported and stored depends on the availability of capture units, compressors, pipelines, and injection facilities. If one of these assets is unavailable, the impact may extend to other parts of the system. This can reduce CO₂ throughput and storage volumes and may also affect the carbon credits associated with the project.

This study develops a deterministic, event-based hourly simulation that represents capture, compression, transport, injection, and storage as a capacity-constrained network. The numerical evaluation uses a transparent synthetic scenario dataset containing 8,760 hourly time steps per scenario for a 1.0 MtCO₂/year system, four system-uptime cases (99%, 97%, 95%, and 90%), five representative downtime events, and carbon prices of USD 30, 60, and 100/tCO₂. At each time step, the algorithm propagates asset availability through the network, identifies the limiting available capacity, integrates verified stored CO₂, and converts the difference from the planned baseline into credit and revenue exposure. For field deployment, the framework specifies an AI/ML pipeline using Isolation Forest for unlabelled sensor-anomaly screening and a gradient-boosted decision-tree classifier for predicting a failure within a defined 24-hour horizon.

The study also looks at ways to reduce the impact of downtime. These include predictive maintenance, digital monitoring, Industrial Internet of Things technologies, and digital twins. Scenario-based analysis is used to examine how better asset availability could help maintain storage capacity, improve system resilience, and reduce potential revenue losses. The paper also considers the reporting and carbon-accounting issues related to downtime as carbon markets develop.

By connecting asset reliability with CO₂ storage and carbon-credit performance, this study shows why equipment availability should be considered throughout the CCUS value chain. The proposed framework can help project teams understand the operational and financial effects of downtime and identify areas where maintenance and monitoring improvements may provide the greatest benefit.

Keyword : Asset Reliability, CCUS Downtime, Carbon Credit Loss, Digital Monitoring Systems, Predictive Maintenance.