Abstract
AbstractProcess safety is concerned with making sure that hazardous chemical industries are designed inherently safer and operated, and maintained by implementing safe operation and maintenance principles. It focuses on preventing events involving leaks, spills, fires, or explosions. Process industries are constantly using the lessons learned from prior mishaps and near misses to stop similar incidents from happening again. A robust near‐miss reporting system at HPCL‐Mittal Energy Limited (HMEL), which owns and operates the Guru Gobind Singh Refinery, enabled the development of an incident predictive model that uses big data to identify areas of safety concern and offer practical insights to help prevent future potential process safety incidents. Through this model, we have demonstrated that if a particular category of process safety‐related near miss, such as unsafe conditions or unsafe acts, are reported more frequently, it is a clear sign to the organization that there may be a potential weakness in the operational discipline, work processes, or safety layers of protection. The methodology also gives organizations the ability to create an action plan to address identified root causes and increase the integrity of safety systems. This paper examines HMEL's successes in preventing future big incidents and provides an illustration of the incident predictive model's operational methodology.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.