A risk factor tracing method for LNG receiving terminals based on GAT and a bidirectional LSTM network

Abstract

LNG terminals are important transfer stations for LNG storage, processing, and outbound transmission which involve intensive manual operation and safety management activities. Traditional safety evaluation methods can provide "cause-effect" information when investigating the traceability of incorrect manual operation. However, manual analysis and summarizing risk factor causality is time-consuming and labor-intensive, and there are inaccuracy problems. Usually, HAZOP meetings require safety, electrical, mechanical, instrumentation and other engineers to brainstorm and summarize the risk factors of LNG receiving terminals. This process is laborious and time-consuming, and there is no guarantee that the person will be able to record it completely every time. In this paper, a graph attention network (GAT) and bidirectional Long and Short-Term Memory Network (LSTM) deep integrated learning model is proposed. The model can automatically extract risk factor causal relationships. After starting with a risk factor text, the number type features and syntactic dependency graph features of the input text are fused to enhance the causal relationship traceability of incorrect personnel operation. Taking the unloading and storage processes as examples, in terms of the effect of automatic extraction risk factors, the integrated algorithm model improves the extraction accuracy by up to 7.1% compared with a single algorithm. In addition, the correct extraction rate of different cause-effect relationships increases by 21.57%, while the integrated algorithm considers sentence matching, improves the granularity of cause-effect information based on the traditional method, expands the root cause of risk factors, and the traceability results are consistent with on-site accident event results. At the same time, it is more conducive to accurately managing incorrect manual operations. In the model training sample size sensitivity analysis, the algorithm has a superior correct causal label extraction rate for different sample sizes when the number of hidden layer stacking layers is K= 2, which solves the problem of model adaptation for the text volume of LNG terminal risk factors.