Risk assessment model for dust explosion in dust removal pipelines using an attention mechanism-based convolutional neural network

Dust explosions occur frequently during production, transportation, and storage processes involving combustible dusts, with dust explosions caused by de-dusting systems being the most common. To prevent such accidents, we need to perform timely and accurate risk assessment. Therefore, we have developed a risk assessment model for dust explosion of dust duct deposition based on convolutional neural network with an attention mechanism (ConvNeXt-Tsc). By enhancing the ConvNeXt block and introducing an attention mechanism, we can more accurately extract the critical features related to the thickness of deposited dust in images of the ducts, achieving a model recognition accuracy of 95.15%. We have verified that the model has a high assessment accuracy in practical applications, which helps to detect potential hazards in dust ducts in time and avoid explosion accidents. The results show that the model has a wide range of application prospects in sedimentary dust explosion risk assessment, with high reliability, practicality, and scientific rigor.