Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks

Abstract

The thermal insulation integrity of liquefied natural gas storage tanks is essential for their life-cycle safety. However, perlite settlement (insulation material) can result in thermal leaks and lead to engineering risks. The direct measurement of perlite settlement is difficult due to the enclosed structure of these tanks. To address this challenge, this study presents a data-driven approach based on machine learning and real-time monitoring data. This study proposes a multi-fidelity machine learning framework to enhance generalizability and leverage multi-fidelity data effectively. Low-fidelity data are readily available but contain systematic errors, while high-fidelity data are accurate but limited in accessibility. By combining both types of data, this framework enhances the generalisability and prediction accuracy of trained models. The results of the data experiments demonstrate that the multi-fidelity framework outperforms models trained solely on low- or high-fidelity data, achieving a coefficient of determination of 0.980 and a root mean square error of 0.078 m. Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. This approach provides a reliable method for the real-time monitoring of thermal insulation integrity in liquefied natural gas storage tanks, contributing to improved industrial safety and operational efficiency.