Novel system design model for an IoT-based real-time oil and gas pipeline leakage monitoring system - O. Effiom, Samuel; A. Fischer, Gertrude; J. Akpama, Eko (International Journal of Engineering and Technology, 2024)
Real-time monitoring of oil and gas pipelines is critical for mitigating the environmental risks posed by leakages. This study presents a novel design model of a real-time Internet of Things (IoT)-based pipeline leakage monitoring system that demonstrates superior detection accuracy, reduced false positives, and operational scalability. The system utilizes pressure and temperature sensors strategically placed along the pipeline to detect anomalies indicative of leakages. By leveraging a hybrid cloud architecture, smart sensors, and machine learning for anomaly detection, the system processes pipeline data in real-time. This data is transmitted in real-time to a central server for analysis, allowing for rapid response to potential hazards. The novelty of this system lies in its hybrid cloud-architecture and the integration of advanced anomaly detection algorithms, which improve accuracy and reduce false alarms. Data from a simulated pipeline setup is analyzed to assess the system's performance, showing a detection accuracy of 94% for simulated leakages. The implications of this study contribute to the knowledge of sustainable operations in the oil and gas sector and provide a scalable framework for future developments.