Background and Aim: Human error often plays an important role in accident causation either through direct action or poor design . The focus of this work is was on prediction of human error probabilities during the process of emergency musters on in gas compressor stations . This paper aims to present a brief description of Human Error Probability Index (HEPI) for the on gas compressor station musters process.

  Materials and Methods : Due to a lack of human error databases, and in particular human error data on gas compressor station musters, an expert judgment technique, the Success Likelihood Index Methodology (SLIM), , was adopted as a means to predict human error probabilities Two muster scenarios of varying severity (gas release, fire and explosion) were studied in detail . A total of 34 reference graphs provided data for both the weighting and rating of six performance shaping factors and the data were subsequently processed by means of SLIM to calculate the probability of success for 16 muster actions ranging from point of muster initiator to the final actions in the temporary safe refuge (TSR). The actions were categorized into 4 phases, namely, awareness, evaluation, egress, and recovery phases. The six performance shaping factors considered in this work were stress, complexity , training, experience, event factors, and atmospheric factors .

  Results: Human error probabilities in the egress phase were highest, followed by those in the evaluation phase; the lowest were in the awareness phase.

  Conclusion: The HEPI can be applied to limit the chances of human error occurrence and mitigate the consequences of such errors through changes in training, design, safety systems, and procedures, resulting in a more error-tolerant design and operation .