Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

Abstract

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.