(562e) Dynamic Risk Analysis: A Case Study On An Industrial Scale Chemical Operation at a Major Petroleum Refinery

In the chemical process industries (CPIs), the extents of human and
financial losses due to accidents are staggering.  Incident investigations have
revealed that every major accident is preceded by many incidents or minor
accidents with low or limited severity.  These are often indicators of
potential accidents known as ?near-misses?.  While many near-misses do
not lead to serious consequences, they always deteriorate the operational
productivity of the process.  Also, recent studies have pointed to the importance
of identifying near-misses to predict the probability of accidents (Meel
and Seider, 2006) and also managing them to reduce the potential of accidents
(Phimister et al., 2003; Cooke and Rohleder, 2006). 

The key premise of this work is that patterns in abnormal events (which
occur when process variables drift out of their normal operating ranges) can be
viewed as near-misses and provide information on both successful
and unsuccessful actions by safety systems (installed to prevent
abnormal behavior), directly or indirectly associated with the process.   To
obtain data on the abnormal events, which have different criticalities, for all
of the measured variables, the dynamic and extensive databases associated with their
distributed control systems (DCSs) and emergency shutdown systems (ESDs), and
incident and accident records, are utilized.  These data are recorded to
monitor the dynamics of the process.   The DCS database contains alarm frequency
data; that is, alarm identity tags for the variables, alarm types (low, high,
high-high, etc.), times at which the variables cross the alarm thresholds (in
both directions), and variable priorities, among others.  The ESD database, of
greater consequence, contains trip (shutdown) data, etc. 

New incident-tracking and learning methods, based on the above databases,
are proposed to develop leading indicators that help identify an escalation in the
probability of occurrence of critical, undesirable events like trips and
accidents ? permitting management and plant operators to take appropriate
actions to prevent upset situations.  They are also used to estimate and
predict the failure probabilities of the safety systems, and more importantly,
the probabilities of the occurrence of trips, using Bayesian theory (Pariyani et
al., 2009).  Furthermore, new key performance indicators over time, which
effectively utilize the abundant information, are proposed to assess and
improve the safety and operational performance of the processing units.

These methods have been successfully applied to
an industrial scale chemical operation at a major overseas petroleum refinery that
processes more than 250,000 barrels of oil per day.  Results and conclusions
from this analysis are presented.    

References

1.   Phimister
J. R., U. Oktem, P. R. Kleindorfer, and H. Kunreuther, ?Near-miss incident
management in the chemical process industry,? Risk Analysis, 23, 445-459
(2003).

2.   Cooke D.L., and T. R. Rohleder,  ?Learning from
incidents: From normal accidents to high reliability.? System Dynamics
Review, 22(3), 213 -239 (2006).

3.   Meel,
A., and W. D. Seider, ?Plant-specific dynamic failure assessment using Bayesian
theory,? Chem. Eng. Sci., 61, 7036-7056 (2006).

4.   Pariyani
A., W. D. Seider, U. G. Oktem, and M. Soroush, ?Synergistic enhancement and assessment
of process safety and product quality,? in preparation (2009).