(54ap) Dynamic Risk Mapping Using Big Data Analytics

Dynamic risk mapping
using Big Data Analytics

Prerna Jain^*, M. Sam
Mannan

Mary Kay O’Connor Process Safety Center

Texas A&M University, College Station, TX-77843-3122, USA

*pjain77@tamu.edu, +1 (979) 985 0773

Abstract

Process
or energy systems such as onshore and offshore production platforms, chemical
processing facilities, utility plants etc. are complex socio-technical systems.
 These systems have several sub-systems or multiple
factors, complex interactions between system components, and their
relationships. The dynamic operations and changing environment continuously
impact the overall risk profile of these systems. Therefore, it is significant
to analyze this emergent behavior and study these socio-technical systems
considering both technical and social as well as internal and external
influences. Not many researchers have explored the use of big data analytics to
map the dynamic risk profiles of these systems. In this work, a systemic novel
methodology is described and developed. For this purpose, the socio-technical system
considered here is reproduced as a system of layers as shown in Figure 1. Based
on this system of layers, dynamic risk profile is obtained by the identification
of weak signals and utilization of the data generated in the facility from
various sources such as historian, Central Maintenance Management System
(CMMS), operational data, Process Safety Management (PSM) system. 

Figure
1: Layered Dynamic Risk Analysis Framework (L-DRAF)

The
evaluation of dynamic risk involves different steps similar to a Layer of
Protection Analysis (LOPA) study. The methodology involves layer-wise analysis
from plant layer to safeguards layer to calculate the final risk as low, medium
or high. In the current paper, analysis for two case studies have been
demonstrated to compare the differences between the conventional and the novel
risk assessment methods. The advantages of the new method are discussed and summarized.

Keywords: Big Data
Analytics, Process Safety; Dynamic Risk Map