A multi-task learning risk assessment method for the chemical process industry

Abstract

Dynamic risk assessment is essential to prevent major accidents in the chemical process industry. The Prediction of accident risk categories, risk likelihood, and risk severity are three fundamental tasks for risk assessment. Single-task learning-based approaches, which merely analyze accident occurrence or quantify the likelihood and severity of an accident risk, often tend to ignore task relevance and inhibit the sharing of critical feature information. This oversight might lead to poor prediction performance. In this study, we propose a multi-task learning model, i.e., Robustly Progressive Layered Extraction (RoPLE), to systematically predict accident risk category, risk likelihood, and risk severity. The RoPLE consists of two parts: a robustly optimized pre-trained module for extracting semantic features from hazard analysis reports (unstructured data) and a progressive layered extraction module for fusing semantic features with features of leading indicators (structured data), which portray the risk state of equipment, workers, environment, and management in the chemical process industry. The progressive layered extraction module contains a multi-level extraction network and a tower network for these three tasks. The multi-level extraction network extracts higher-level shared information, while the tower network extracts task-specific feature information. Empirical experiments demonstrate the feasibility of our model on a real risk assessment dataset in Beijing’s chemical process industry. The RoPLE obtains high F1-scores of 63.1%, 79.5%, and 86.2% for the three tasks, and significantly outperforms other state-of-the-art single-task methods.