Automating HAZOP analysis of batch chemical plants: Part I. The knowledge representation framework

Abstract

Hazard and operability (HAZOP) analysis is a systematic procedure for determining the abnormal causes of process deviations from normal behavior and their adverse consequences in a chemical plant. This is a difficult, labor-intensive, time-consuming activity that would benefit from automation. While HAZOP analysis is generally applied only to continuous process plants, it can be generalized to include batch process systems as well, as shown in this paper. The main thrust of this paper, however, is to develop a general framework for automating the HAZOP analysis of batch plants. The proposed framework combines high-level Petri nets and digraphs with object-oriented knowledge representation for the development of a general, flexible, efficient, and user-friendly system, called Batch HAZOPExpert , implemented in G2. In this framework, the knowledge about tasks and sub-tasks in a batch process are modeled hierarchically using high-level Petri nets. Cause and effect relationships between process variables within a subtask are represented using subtask digraphs. Petri nets and subtask digraphs interact with each other in a two-tier organization to model the behavior of batch processes. One of the novel features of this framework is that both the continuous and discrete nature of batch operation are represented explicitly. Another feature is the modeling of operator actions and errors, since they play a more crucial role in batch process systems than in continuous ones. The first part of this paper describes the details of the intelligent systems framework. The second part discusses the application of the Batch HAZOPExpert system to an industrial case study.