In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the relationships among the model variables. By solving a bi‐objective optimization problem applying the genetic algorithm (GA) with the Pareto ranking approach, the network structure was learned. Then, risk modeling was performed for a petroleum refinery focusing on HydroDeSulfurization (HDS) technology throughout its life cycle. To extend the model horizontally and make it possible to evaluate the risk trend throughout the technology life cycle, we developed a dynamic Bayesian network (DBN) with three‐time slices. A two‐way forward and backward approach was used to analyze the model. The model validation was performed by applying the leave‐one‐out cross‐validation method.

中文翻译：

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使用动态贝叶斯网络的整个系统生命周期中的前向和后向风险评估：石油精炼厂中的一个案例

在本文中，风险建模是基于概念风险框架中定义的风险要素进行的。该模型可以估算各种风险，包括人为错误概率，操作风险，财务风险，技术风险，商业风险，健康风险以及社会和环境风险。贝叶斯网络（BN）结构学习技术用于确定模型变量之间的关系。通过使用遗传算法（GA）和Pareto排序方法解决双目标优化问题，学习了网络结构。然后，针对一家炼油厂在其整个生命周期内专注于加氢脱硫（HDS）技术的风险建模。为了水平扩展模型并可以评估整个技术生命周期中的风险趋势，我们开发了具有三个时间片的动态贝叶斯网络（DBN）。使用双向前进和后退方法分析模型。通过应用留一法交叉验证方法进行模型验证。