Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

In order to effectively predict fire risk of hydrogen pipeline leakage and improve the safe level of hydrogen pipeline, the finite Ridgelet neural network optimized by improved firefly algorithm is constructed. Related researches on pipeline leakage fire risk and corresponding prediction models are summarized. The finite Ridgelet neural network is constructed by using finite Ridgelet function as excitation function of node in hidden layer, and the structure of finite Ridgelet neural network is established. The improved firefly algorithm based on location update mechanism is designed to optimize the parameters of finite Ridgelet neural network. And then the fire risk level prediction procedure of hydrogen pipeline is designed. Finally, forty training samples and ten testing samples are selected to carry out fire risk level prediction analysis, results show that the proposed finite Ridgelet neural network optimized by improved firefly algorithm has advantages in prediction precision and accuracy, which can effectively predict the safety status of hydrogen pipeline. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a finite Ridgelet neural network optimized by improved firefly algorithm for predicting the fire risk of hydrogen pipeline leakage. By summarizing related research, a finite Ridgelet neural network is constructed and a fire risk level prediction procedure is designed. Experimental results show that the proposed network has advantages in prediction precision and accuracy, and can effectively predict the safety status of hydrogen pipeline.