AISYAH MARYAM BINTI MOHAMED HASSAN Universiti Teknologi PETRONAS (UTP)
Carbon Capture and Storage (CCS) is essential for reducing industrial CO2 emissions, yet post-injection monitoring remains manual, time-consuming, and error-prone. This project automates the full workflow of carbon injection data processing using UiPath, integrating AI-based anomaly detection using Isolation Forest, automated calculations, and a continuously updated Power BI dashboard. The system completes processing in 15 minutes, saving 55 minutes per cycle while eliminating human error and enhancing near real-time visibility of injection performance. By focusing on the underdeveloped carbon injection phase and delivering a fully automated, data-driven solution, the system demonstrates strong operational and commercial value for CCS operations.