An online real-time estimation tool of leakage parameters for hazardous liquid pipelines

Abstract

Hazardous liquid pipeline (HLP) leaks not only result in energy waste and environmental pollution, but also pose a threat to people's lives and property. The estimation of leakage parameters is an essential part of risk assessment and environment pollution assessment. However, current common leak detection methods are mainly based on physical models with assumptions and are susceptible to noise. Limited historical leakage data render it impossible to develop a leak model in advance. To address this problem, this study establishes a pipeline digital twin model that simulates a pipeline leak to generate leakage data. A conditional variational auto-encoder (CVAE) framework is proposed to estimate the leakage parameters based on data detected by upstream and downstream meters once the HLP leak occurs. CVAE can treat the high-dimensional detected data as labels to overcome the dimensionality problem. Based on the CVAE framework, an online real-time leakage parameter estimation tool for HLP is formed. To qualify the performance of the approach, a sensitivity analysis for the structure of the CVAE framework is evaluated. Finally, four examples demonstrate the effectiveness, stability, and applicability of the proposed method.