Prediction of venting gas explosion overpressure based on a combination of explosive theory and machine learning

Combustible gas is widely used in industry and daily life but presents an unavoidable explosion hazard. Predicting explosion overpressure and guiding engineering design is an effective way to reduce the consequences of explosions. This paper presents a novel approach that integrates explosive theory and data science. Starting from the first principle, a dimensionless mathematical formula was constructed based on the explosive theory. Gas species, gas concentration, container parameters, and ignition position were fully considered by the formula, and built into data sets using machine learning techniques as characteristic parameters. The SVR algorithm, the BP algorithm, and the DNN algorithm all attest to the superiority. And the predictive physical consistency of this method has been proven. Simultaneously, the dataset constructed by this method effectively reduces the number of features. The PSO algorithm was used for parameter optimization based on the DNN algorithm guided by theory for building F-P (First principle) PSO-DNN Model. In the comparison of prediction results, the mean relative error of the F-P PSO-DNN model is 15%, 19% lower than that of the best performing Molkov model.

Automated summary

This paper presents a novel approach that integrates explosive theory and data science to predict explosion overpressure and guide engineering design. A dimensionless mathematical formula was constructed based on explosive theory and used in data sets to consider various parameters. The predictive accuracy of this method was proven and showed better results compared to other models.