Dynamic Risk Analysis in the Chemical and Petroleum Industry
Static, incomplete, superficial, fictitious, or simply wrong. Application of risk analysis to process industries has been largely criticized and blamed in response to recent major accidents. Since it was first proposed, modifications and improvements have been made, and a generally accepted approach is considered in several regulations and standards. However, research in this field keeps producing new tools and techniques to overcome the flaws and deficiencies encountered in decades of use.
Risk analysis is about to enter an era of big data, where the main challenges are represented by the ability to provide continuous acquisition, effective process, and meaningful communication of such information. A new generation of wireless sensors, increasingly powerful computers, and optical fibers are only some examples of industry potential.
The focus of this book is the emerging topic of dynamic risk analysis, as opposed to traditional risk analysis, which is incapable of reflecting constantly evolving real-world risk. Dynamic risk analysis is often confused with online monitoring of representative indicators, but the adjective dynamic has a deeper significance, according to Merriam- Webster’s, (of a process or system) characterized by constant change, activity, or progress. Most risk analysis steps have the potential to be performed in a dynamic fashion. Inputs and models may be continuously improved, calibrated, and corrected based on new related evidence and lessons learned to progressively adapt to the ever-changing reality.
A wider range of data may be considered: not only technical, but also human and organizational, information allowing for a proactive perspective. In addition, results may outline the economic impact of risk management to promote safety-oriented company strategies.
Although dynamic risk management is common practice in finance as a response to the financial crisis in 2008, in a safety-critical sector such as the chemical and petroleum industry, most methods for quantitative risk assessment mainly provide static evaluations. Standards (eg, ISO 31000 on risk management and NORSOK Z-013 on risk and emergency preparedness analysis) and relevant regulations (eg, the European Union’s Seveso directives on the control of major-accident hazards involving dangerous substances) suggest updates of risk analysis, but mostly in conjunction with major changes in the plant or organization.
“Google Trends” shows that the number of Google searches for the term “big data” has increased about 100 times since 2011, and today it has reached its peak. On the contrary, the term “dynamic risk” had its peak in 2009 (after the financial crisis), and today its popularity on the search engine has decreased by about one third. Although many factors may affect such trends and do not represent the actual applications, this may reflect the challenges of dynamic risk analysis in finding its place in standard industrial approaches.
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