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

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-22 DOI:10.1007/s00477-024-02781-5

Yang Li, Gaozhi Cui, Qinglin Han, Simeng Chen, Shuaishuai Lu

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Abstract

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.

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利用基于注意机制的卷积神经网络建立除尘管道粉尘爆炸风险评估模型

粉尘爆炸经常发生在涉及可燃粉尘的生产、运输和储存过程中，其中以除尘系统引起的粉尘爆炸最为常见。为防止此类事故的发生，我们需要及时、准确地进行风险评估。因此，我们开发了一种基于卷积神经网络和注意力机制（ConvNeXt-Tsc）的粉尘管道沉积粉尘爆炸风险评估模型。通过增强 ConvNeXt 块并引入注意机制，我们可以更准确地提取管道图像中与沉积粉尘厚度相关的关键特征，模型识别准确率达到 95.15%。我们在实际应用中验证了该模型具有较高的评估精度，有助于及时发现粉尘管道中的潜在危险，避免爆炸事故的发生。结果表明，该模型在沉积粉尘爆炸风险评估方面具有广泛的应用前景，具有较高的可靠性、实用性和科学严谨性。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.