Factors affecting human reliability in the chemical process industry

Abstract

This research was concerned with identifying factors which may influence human reliability within chemical process plants - these factors are referred to as Performance Shaping Factors (PSFs). Following a period of familiarization within the industry, a number of case studies were undertaken covering a range of basic influencing factors. Plant records and site `lost time incident reports' were also used as supporting evidence for identifying and classifying PSFs. In parallel to the investigative research, the available literature appertaining to human reliability assessment and PSFs was considered in relation to the chemical process plan environment. As a direct result of this work, a PSF classification structure has been produced with an accompanying detailed listing. Phase two of the research considered the identification of important individual PSFs for specific situations. Based on the experience and data gained during phase one, it emerged that certain generic features of a task influenced PSF relevance. This led to the establishment of a finite set of generic task groups and response types. Similarly, certain PSFs influence some human errors more than others. The result was a set of error type key words, plus the identification and classification of error causes with their underlying error mechanisms. By linking all these aspects together, a comprehensive methodology has been forwarded as the basis of a computerized aid for system designers. To recapitulate, the major results of this research have been: One, the development of a comprehensive PSF listing specifically for the chemical process industries with a classification structure that facilitates future updates; and two, a model of identifying relevant SPFs and their order of priority. Future requirements are the evaluation of the PSF listing and the identification method. The latter must be considered both in terms of `useability' and its success as a design enhancer, in terms of an observable reduction in important human errors.