液化石油气（LPG）作为一种环保型能源，在我国的能源消费结构中扮演着重要角色。化工园区LPG生产、储运过程具有规模大、流程长、工况复杂、扩散性强、易燃易爆，和应急救援复杂等特点，随着LPG产业需求规模的扩大，如何避免由LPG球罐泄漏导致的爆炸、火灾和中毒等次生多米诺灾害事故发生，不仅是技术层面的需求，更是公共安全和社会和谐的重要举措。本文构建了化工园区LPG球罐区域风险评价与动态风险预警模型，开发了化工园区LPG球罐泄漏耦合风险动态管控平台，实现了基于数据驱动的化工园区LPG球罐区泄漏耦合风险实时动态预警与精准管控，对于实现涉LPG企业安全长远发展具有重要意义。论文主要研究工作如下：

（1）分析了近10年LPG储罐事故的特征与规律。通过收集我国2014-2023年发生的1050起各类LPG储罐事故案例，分别从事故的储罐类型、时间分布、操作状态、耦合类型等方面进行分析，得到LPG储罐事故的特征与统计规律。基于ARMA模型对LPG储罐事故时间序列预测分析，预测各种类型的LPG储罐事故5年内每个季度事故起数和伤亡人数的变化趋势，为建立化工园区LPG球罐泄漏致因信息库提供理论基础。

（2）建立了化工园区LPG球罐泄漏致因信息库。依据5W2H分析法，基于事故统计分析和现场访谈，运用扎根理论，经过三级编码通过整理、归纳，建立了由7个核心范畴以及22个主范畴、74个范畴组成的基于系统性视角的化工园区LPG球罐泄漏事故致因信息集，并构建了化工园区LPG球罐泄漏致因因素概念模型，为后续LPG球罐泄漏致因演化机制与风险管控事故预测研究提供基础信息。

（3）揭示了化工园区LPG球罐泄漏致因层级关系、属性特征、耦合强关联规则。针对化工园区内的LPG球罐泄漏，综合采用DEMATEL（决策实验室分析）、ISM（解释结构模型）、MICMAC（相互依赖性矩阵分析）与关联规则Apriori算法方法，基于近10年发生的207起LPG储罐泄漏事故致因数据，得到了事故致因因素的五个不同的层级，明晰了致因的属性特征、耦合方式以及耦合关联规则。通过设置支持度等关联规则指标大小，得到了LPG储罐泄漏事故发生的强关联规则与高频风险因素集合，为后续LPG球罐泄漏致灾风险演化路径和风险评估与SD风险演化组合干预仿真模拟提供参数学习信息。

（4）建立了化工园区LPG球罐泄漏事故演化模型。使用基于结构方程模型-贝叶斯网络（SEM-BN）方法验证和修正了致因因素概念模型中LPG球罐安全水平与储罐泄漏事故之间影响关系的潜在变量路径；验证了应急救援水平在事故演化路径中的中介作用。结合专家经验知识、最大似然(EM)估计算法，实现了风险演化路径的量化，确定了风险演化过程中敏感性较高与风险传递过程中的关键路径，为构建LPG球罐区域风险评估模型与动态预警模型的指标体系建立提供理论依据。

（5）构建了基于数据驱动的化工园区LPG球罐区域风险评价模型。基于化工园区LPG球罐泄漏致因识别与风险演化路径量化研究，构建了包含物-人-机-环-管-应-监7个一级风险因素、22个二级风险因素、74个指标的区域风险评价指标体系。通过对比四种不同优化模型在误差分布、时间成本等方面的差异，确定了基于数据驱动的具有最高准确性的t-SNE和IGWO-MLP神经网络模型为化工园区LPG球罐安全风险评价模型，为储罐泄漏事故风险管控的组合干预策略提供了管控依据。

（6）构建了化工园区LPG球罐IGWO-LSSVM风险预警模型。选取具有时效性的17个预警指标作为LPG球罐动态风险预警模型指标，建立了最小二乘支持向量（LSSVM）多分类动态风险预警模型，采用改进的灰狼算法（IGWO）对模型的超参数进行自动优化，形成了一个动态高精度的LPG球罐IGWO-LSSVM风险预警模型。为后续风险管控平台的建立提供动态精准预判风险的理论基础。

（7）实现了化工园区LPG球罐泄漏耦合风险动态管控。通过前期的致因分析与风险演化机制研究，基于化工园区LPG球罐区域风险评估与动态预警模型，结合系统动力学理论运用组合干预方法干预风险演化路径的思路，借助SpringBoot+Vue前后端分离的Java快速开发框架开发了LPG球罐泄漏耦合风险动态管控平台。选取我国一家煤制油企业进行实证研究，通过储罐区域风险评价与储罐动态风险预警模型的结合，运用Vensim-PLE的仿真实现LPG球罐泄漏耦合风险组合干预动态管控，企业实现了从被动到主动、从分散到集中、从静态到动态的管理转变。全面增强风险管控和安全生产水平。以期为化工领域储罐泄漏事故的预先防控提供理论支撑。

Liquefied petroleum gas (LPG), as an environmentally-friendly energy source, plays a significant role in the energy consumption structure of our country. The processes of production, storage, and transportation of LPG in chemical industrial parks are characterized by large scale, lengthy procedures, complex working conditions, strong diffusibility, flammability, explosiveness, challenges in emergency response. With the rising demand for LPG, addressing secondary disaster risks, such as explosions, fires, and poisonings resulting from LPG tank leaks, is essential for both technical safety and public well-being. This paper introduces a regional risk assessment and dynamic early-warning model for LPG spherical tanks in chemical industrial parks. Additionally, a dynamic risk control platform was developed to enable real-time monitoring and precise control over the coupled risks associated with LPG tank leaks, leveraging data-driven methodologies. This model contributes significantly to ensuring the long-term safety of LPG enterprises. The main research objectives of this paper are as follows:

(1) Analysis of LPG tank accident characteristics over the past decade. Based on data from 1,050 LPG tank accident cases in China between 2014 and 2023, an analysis was conducted covering accident types, timing, operational status, and coupling characteristics. Using the ARMA model for time-series prediction, trends in accident frequency and casualty rates across different tank types for the next five years were projected, offering a theoretical basis for risk mitigation planning.

(2) Development of a causal information database for LPG spherical tank leaks in chemical parks. A comprehensive database detailing the causes of LPG spherical tank leaks within chemical parks was created using 5W2H analysis and grounded theory. The data from LPG tank accidents and field interviewing underwent a systematic three-tier coding process, culminating in an information structure with 7 core categories, 22 main categories, and 74 subcategories. Additionally, a conceptual model was formulated to map the contributing factors to LPG tank leaks, providing critical data for deeper investigation into causal mechanisms and predictive risk control measures.

(3) Analysis of causal hierarchies and strong coupling associations in LPG tank leaks. Advanced methodologies, including DEMATEL, ISM, MICMAC, and the Apriori algorithm, were employed to reveal the layered causal relationships, attribute characteristics, and coupling association patterns of LPG tank leaks. Data from 207 LPG tank leak incidents over the past decade led to the identification of five accident-causation levels. This in-depth analysis highlighted specific coupling patterns and high-frequency risk factors, offering essential parameters for further simulation studies in disaster evolution pathways and integrated risk assessment.

(4) Development of a model of the evolution for LPG spherical tank leaks in chemical parks.Quantification of causal coupling evolution pathways for LPG tank leaks. The structural equation model–bayesian network (SEM-BN) approach was utilized to verify and refine latent-variable pathways, exploring the influence of safety measures on LPG tank leaks within the conceptual causal framework. This analysis confirmed the mediating impact of emergency response levels on accident progression. By integrating expert knowledge with the EM estimation algorithm, key, high-sensitivity paths were identified in both the risk evolution and transmission processes. These findings provide a theoretical framework for constructing regional risk assessment indices and dynamic early-warning models tailored to LPG tank safety.

(5) Formulation of a data-driven regional risk assessment model for LPG storage tanks in chemical parks. A specialized model was formulated to assess regional risks associated with LPG spherical storage tanks within chemical parks. This framework stems from in-depth research into the identification of causative factors and quantification of risk evolution paths in storage tank leakage scenarios. A comprehensive risk assessment index was constructed, covering seven primary risk dimensions—material, human, mechanical, environmental, managerial, response, and supervisory factors—alongside 22 secondary elements and 74 specific indicators. Upon examining error distribution, time efficiency, and other criteria across four distinct optimization approaches, the t-SNE and IGWO-MLP neural network models demonstrated superior accuracy for safety risk assessment, providing a structured basis for an integrated approach to mitigating tank leak risks.

(6) Formulation of an IGWO-LSSVM risk early-warning model for LPG storage tanks in chemical parks. A high-precision, early-warning framework was designed to enable dynamic risk monitoring for LPG storage tanks, utilizing seventeen key indicators. This multi-class dynamic risk early-warning model, based on least squares support vector machine (LSSVM) algorithms, underwent automated optimization via the improved grey wolf optimization (GWO) method. The resulting IGWO-LSSVM model offers a theoretically robust foundation for anticipatory risk management, with potential applications in a broader risk prediction platform for LPG storage facilities.

(7) A dynamic coupling risk management platform for LPG storage tank leaks in chemical industrial parks. Drawing from extensive causal analysis and risk evolution research, this system integrates regional risk assessments and dynamic early-warning mechanisms to manage the coupled risks associated with LPG tank leaks. Leveraging the SpringBoot + Vue framework, the platform renders a dynamic intervention structure based on system dynamics and combined intervention methods. In an empirical application within a coal-to-liquids enterprise in China, the platform demonstrated its capability by linking regional risk assessments with real-time warnings. Simulation through Vensim-PLE facilitated the proactive control of LPG tank leak risks, offering substantial theoretical and practical support for mitigating storage tank incidents in the chemical sector. Enterprises have realized the management transformation from passive to active, from decentralized to centralized, from static to dynamic. We will comprehensively strengthen risk management and control and work safety. To provide theoretical support for the prevention and control of tank leakage accidents in chemical industry.

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