Process safety and risk assessment are major requirements in the industrial context and hazard identification is essential for ensuring safe design and operation of a process. Numerous automated software approaches to risk assessment have resulted in many improvements in human brainstorming techniques of conventional risk assessment. In terms of computing time as an important aspect of these automated tools, mathematical simulation of physical and chemical states of the process is most time consuming in comparison to results collection and evaluation. In this context, GPU parallel computing has many advantages which meet the demanding requirements on complex and precise process hazard analysis without the involvement of massive processing architectures. This paper presents an efficient low-cost way of significant acceleration of targeted prediction of incident consequences by dynamic simulation of process fault deviations in the context of safety analysis. The GPU based simulation computing algorithm acceleration has been demonstrated on a hazard and operability analysis of propylene glycol production carried out in a closed loop CSTR (continuous stirred tank reactor). In this application, HAZOP multi-parameter process deviations have been simulated 100 times faster than when using its CPU code version.

过程安全和风险评估是工业环境中的主要要求，危害识别对于确保过程的安全设计和操作至关重要。许多用于风险评估的自动化软件方法已使传统风险评估的人类头脑风暴技术得到了许多改进。就将计算时间作为这些自动化工具的重要方面而言，与结果收集和评估相比，对过程的物理和化学状态进行数学模拟最耗时。在这种情况下，GPU并行计算具有许多优势，无需复杂的处理架构即可满足复杂而精确的过程危害分析的苛刻要求。本文提出了一种有效的低成本方法，通过在安全分析的情况下对过程故障偏差进行动态仿真，可以显着加速对事件后果的目标预测。在闭环CSTR（连续搅拌釜反应器）中进行的丙二醇生产的危害和可操作性分析中，已经证明了基于GPU的仿真计算算法的加速。在此应用中，与使用其CPU代码版本相比，HAZOP多参数过程偏差的仿真速度快了100倍。在闭环CSTR（连续搅拌釜反应器）中进行的丙二醇生产的危害和可操作性分析中，已经证明了基于GPU的仿真计算算法的加速。在此应用中，与使用其CPU代码版本相比，HAZOP多参数过程偏差的仿真速度快了100倍。在闭环CSTR（连续搅拌釜反应器）中进行的丙二醇生产的危害和可操作性分析中，已经证明了基于GPU的仿真计算算法的加速。在此应用中，与使用其CPU代码版本相比，HAZOP多参数过程偏差的仿真速度快了100倍。