Chemical warehouses are one of the high-risk areas in the process industries due to the high diversity and quantity of stored chemicals. Risk assessment is a useful tool for developing appropriate strategies to prevent and control the risks. In this study, computational fluid dynamics (CFD) and Bayesian network (BN) approaches were proposed for dynamic risk assessment. Initially, bow tie (BT) method was used for identifying basic events and modeling the consequences. In order to determine the consequences intensity (heat flux and CO and CO₂ concentration), fire dynamics simulator (FDS) and solid flame model were used. A total of 21 causes or failures were identified in the chemical spills, 13 cases of which were related to basic events. Out of the identified causes, the forklift and drum strike basic event had the most contribution of the chemical spills in the warehouse, and the probability of the major spill event scenario 1.25495E−11 was estimated by Bayesian networks. The estimated risk by CFD in combination with BN, after updating, is unacceptable compared to the UK risk criterion. Bayesian networks and CFD approach for dynamic assessment of environmental impact risk in chemical warehouses provide the capability to quantitatively and dynamically assess the consequences of chemical spills by modeling its cause and effect in the warehouse. Based on the results of this method, effective preventive measures can be taken to control the consequences of chemical spills in the warehouse.

由于所存储化学物质的多样性和数量高，化学物质仓库是加工业中的高风险区域之一。风险评估是制定适当策略来预防和控制风险的有用工具。在这项研究中，提出了计算流体动力学（CFD）和贝叶斯网络（BN）方法进行动态风险评估。最初，领结（BT）方法用于识别基本事件并建模后果。为了确定后果强度（热通量以及CO和CO ₂浓度），火灾动力学模拟器（FDS）和固体火焰模型。在化学品泄漏中总共发现了21个原因或故障，其中13例与基本事件有关。在确定的原因中，叉车和鼓罢工的基本事件对仓库中化学品泄漏的贡献最大，贝叶斯网络估计了重大泄漏事件情景1.25495E-11的可能性。与英国的风险标准相比，更新后的CFD结合BN估算的风险是不可接受的。用于化学仓库环境影响风险动态评估的贝叶斯网络和CFD方法提供了通过对仓库中化学品泄漏的因果进行建模来定量和动态评估化学泄漏后果的能力。根据此方法的结果，