Real-time monitoring of CO2 transport pipelines using deep learning

• DOI: 10.1016/j.psep.2023.11.024
• Publication Date: 2023-11-17T10:31:15Z
• Authors: Juhyun Kim; Hyunjee Yoon; Saebom Hwang; Daein Jeong; Seil Ki; Bin Liang; Hoonyoung Jeong
• Keywords: Anomaly (physics)

Real-time pipeline monitoring is important for the safe transportation of captured CO2. A dynamic modeling method, which one methods, can provide reliable diagnostic results various anomalies. In anomalies are detected by comparing predictions and observations variables. However, licensing costs associated with use flow simulators that provides high. this study, we developed a real-time deep-learning-based method save cost simulators. The obtained using deep-learning models where simulator required only in training step. Two improvements were made to enhance both prediction anomaly detection accuracies. First, accuracy variables be improved considering delay time interval between inlet outlet points pairing input output data. Second, also conditionally choosing based on normal operation ranges observations. As part field demonstration, proposed was applied CO2 transport located Donghae-1 gas field. showed more than 25%.