Hazard and Operability (HAZOP) studies are conducted to identify and assess potential hazards which originate from processes, equipment, and process plants. These studies are human-centered processes that are time and labor-intensive. Also, extensive expertise and experience in the field of process safety engineering are required. There have been several attempts by different research groups to (semi-)automate HAZOP studies in the past. Within this research, a knowledge-based framework for the automatic generation of HAZOP worksheets was developed. Compared to other approaches, the focus is on representing semantic relationships between HAZOP relevant concepts under consideration of the degree of abstraction. In the course of this, expert knowledge from the process and plant safety (PPS) domain is embedded within the ontological model. Based on that, a reasoning algorithm based on semantic reasoners is developed to identify hazards and operability issues in a HAZOP similar manner. An advantage of the proposed method is that by modeling causal relationships between HAZOP concepts, automatically generated but meaningless scenarios can be avoided. The results of the enhanced causation model are high quality extended HAZOP worksheets. The developed methodology is applied within a case study that involves a hexane storage tank. The quality and quantity of the automatically generated results agree with the original worksheets. Thus the ontology-based reasoning algorithm is well-suited to identify hazardous scenarios and operability issues. Node-based analyses involving multiple process units can also be carried out by a slight adjustment of the method. The presented method can help to support HAZOP study participants and non-experts in conducting HAZOP studies.

进行危害与可操作性（HAZOP）研究以识别和评估源自过程，设备和过程工厂的潜在危害。这些研究是以人为中心的过程，这些过程既费时又费力。此外，还需要过程安全工程领域的广泛专业知识和经验。过去，不同的研究小组已经进行了几次尝试来使HAZOP研究（半）自动化。在这项研究中，开发了一种基于知识的框架，用于自动生成HAZOP工作表。与其他方法相比，重点是在考虑抽象程度的情况下表示HAZOP相关概念之间的语义关系。在此过程中，来自过程和工厂安全（PPS）领域的专家知识被嵌入到本体模型中。在此基础上，开发了一种基于语义推理器的推理算法，以类似于HAZOP的方式识别危险和可操作性问题。该方法的优点是，通过对HAZOP概念之间的因果关系建模，可以避免自动生成但毫无意义的情况。增强的因果关系模型的结果是高质量的扩展HAZOP工作表。所开发的方法应用于涉及己烷储罐的案例研究中。自动生成的结果的质量和数量与原始工作表一致。因此，基于本体的推理算法非常适合于识别危险场景和可操作性问题。通过稍微调整方法，也可以进行涉及多个过程单元的基于节点的分析。