It has been recognized that traditional hazard-analysis methods have had drawbacks because they are arduous, tedious, and time-consuming works, require multidisciplinary knowledge, and demand considerable cognitive efforts from the analysts. To overcome these problems, using multiple modeling concepts, new process-knowledge representation models for hazard analysis are devised. The models consist of the unit function model, the unit behavior model, the process structure model, and the process material model. They are used to describe chemical processes from a safety-oriented paint of slew. From these models, three hazard-analysis algorithms (deviation analysis. malfunction analysis, and accident analysis algorithm) are proposed. In this article, the overall system, which is embodied using G2 language, is described and applied to olefin dimerization plants. The results show that more possible accidents can be identified and that the developed methodology has the ability to capture process hazards in terms of both functional failure and unexpected variable deviations, thereby improving the quality of the hazard analysis.