Of the numerous inherent safety assessment tools, a dynamic metric capable of investigating and incorporating the temporal risk evolution when conducting Inherently Safer Modifications (ISMs) is yet to be established. To this end, this work developed a Dynamic Inherent Safety Metric (DISM) and validated its functionality and viability through a case study. Firstly, the Information-Flow-based Accident-causing Model (IFAM) was adapted to construct the topology of Bayesian Networks (BN). Then, Bayesian deductive reasoning was executed to do crucial risk identification by ranking posterior probabilities. Finally, risk-based ISMs were performed to address the relatively contributing risk factors. The case study results show that the fire and explosion risk decreased by approximately a third after implementing ISMs, thus demonstrating that the modified processing scenario could be inherently safer than the original processing scenario. The newly developed inherent safety metric (i.e., DISM) can assist in temporal risk identification and assessment, and it is expected to function as a novel assessment tool for measuring and comparing the inherent safeness before and after implementing ISMs with simultaneous considerations on the time-varying risk factors.

在众多固有的安全评估工具中，尚待建立一种能够在进行固有安全性修改（ISM）时调查并纳入时间风险演变的动态指标。为此，这项工作开发了动态固有安全性度量标准（DISM），并通过案例研究验证了其功能和可行性。首先，将基于信息流的事故致因模型（IFAM）应用于构建贝叶斯网络（BN）的拓扑。然后，执行贝叶斯演绎推理，通过对后验概率进行排序来进行关键风险识别。最后，基于风险的ISM被执行以解决相对贡献的风险因素。案例研究结果表明，实施ISM后，火灾和爆炸风险降低了大约三分之一，因此证明了修改后的处理方案在本质上比原始处理方案更安全。新开发的固有安全性度量标准（即DISM）可以帮助进行时间风险识别和评估，并且有望作为一种新颖的评估工具，用于测量和比较实施ISM前后的固有安全性，同时考虑时间因素。变化的风险因素。