Abstract Chemical accidents occur frequently in China owing to inadequate process safety risk management and control in warehouses. Real-time dynamic risk assessment can identify stored process risks and reduce the accident probability. The support vector machine (SVM) is an effective dynamic risk assessment method. To improve the dynamic risk assessment performance of the SVM model, the electrostatic discharge method (ESDA), which has a strong optimization ability, was used to optimize the model parameters. An improved mixed kernel (NP mixed kernel) that was a linear combination of the novel radial basis function and polynomial kernel was constructed, and an intelligent assessment model of the warehouse fire dynamic risk based on the ESDA and improved SVM (ESDA-NPSVM) was proposed. The experimental results indicated that the proposed model had excellent performance for the dynamic risk assessment of fire accidents in Class A hazardous chemical warehouses, suggesting that it is useful for practical applications.

中文翻译：

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基于静电放电法和改进支持向量机的危化品仓库火灾动态智能风险评估

摘要 由于仓库过程安全风险管控不力，我国化工事故频发。实时动态风险评估可识别存储过程风险，降低事故概率。支持向量机（SVM）是一种有效的动态风险评估方法。为提高SVM模型的动态风险评估性能，采用优化能力强的静电放电法(ESDA)对模型参数进行优化。构建了新型径向基函数和多项式核的线性组合的改进混合核（NP混合核），建立了基于ESDA和改进SVM的仓库火灾动态风险智能评估模型（ESDA-NPSVM）建议的。