Learning Hazop Expert System By Case-based Reasoning And Ontology

摘要

To improve the learning capability of HAZOP expert systems, a new learning HAZOP expert system called PetroHAZOP has been developed based on the integration of case-based reasoning (CBR) and ontology that can help automate "non-routine" HAZOP analysis. PetroHAZOP consists of four modules including case base module, CBR engine module, knowledge maintenance module and user graphical interface module. Within the case base, HAZOP analysis knowledge is represented as cases which are organized with a hierarchical structure. Similarity-based case retrieval algorithm is also depicted to find the closest-matching cases. In order to enhance the case retrieval, a new set of ontologies for CBR-based HAZOP analysis is created by integration of existing ontologies reported in literature. Finally the application of PetroHAZOP is demonstrated by two case studies of industrial processes.