(5a) Preventing Process Safety Incident Using Nearmiss/Unsafe Condition Big Data Analysis.

Process safety Near miss data including unsafe condition/act are very crucial in the operating plant and if reported correctly, it can give real insight of the performance of organizations key work processes, discipline of operational professionals & contractors, and finally strength of layers of protection installed to prevent the incident.

This data if analyst properly can be used to provide an early indication of problems that can be corrected before a major process safety incident occurs.

Other than external causes, the process safety incident are caused due to human error and equipment performance failure. In HMEL, process safety unsafe condition and acts are categorized into human error producing condition and the basic causes of equipment malfunction which includes loss of containment (Below tier 3 threshold), equipment /instrument malfunction, vibration, improper blinds/bolts/support/tools etc.

Above data is further analyst to predict the process safety incident in specific area of facility.

When co-related the above near miss data with the major (tier -1, tier 2 and tier 3) incidences, we found that process safety related near miss data was higher when the number of incidences (LOPC) were high in HMEL. This analysis suggests that there is direct co-relation with the near miss and the major incidences.

If there is increase in the near misses (specific category) it indicates there is a gap in the work process / operational discipline / gap in Layer of protections. It is the indication that the organization has to focus on near miss causes and address it on time or it may result in Process safety incidence.

The process safety near-miss analysis is helping HMEL to identify & pin point the area of concern and gives the information about potential process safety incident before it is happening.

The analysis also helps to sensitize the team in issue of concern for process safety.

HMEL has able to prevent few catastrophic failures before its occurrence with the help of this type of process safety near miss analysis.

The presentation include the HMEL program for development of big data, analysis in HMEL way & few success story of HMEL where we able to prevent process safety incident before its happening.