(688a) Synergistic Improvement of Process Safety and Product Quality

Major objectives to reduce the economic and human
losses in the chemical process industries are to improve the mechanisms for process
safety as well as product quality control.  Quality defects are
flagged by ?special causes? (arising due to failures of hardware components,
errors by operators in performing operating procedures, inefficient management
[1], etc.) in the statistical process control mechanisms.  In many respects,
these are closely related to the safety problems, which are
flagged by ?near-misses? (indicators of potential accidents).  Prior research has emphasized the importance of identifying
these near-misses, high-probability, low-consequence events, in
predicting and preventing the occurrence of catastrophic, low?probability,
high-consequence events [2, 3].  This paper quantifies the relationship between
special-causes and near-misses and proposes a novel approach to integrate
and enhance techniques for safety and quality, thereby helping to improve the
predictability of potential accidents.

An approach to identify potential special-causes
detectable from product-defect information and determine the relative
significance and probabilities of the occurrence of the near misses, including
failure probabilities of safety systems, is presented.  This
approach involves utilizing dynamically available product-quality and safety data,
and exploiting the pattern information from near-misses, often
overlooked in product defects.  Abnormal events that, otherwise, may lead to
off-specification products and accidents, are often prevented using predictions
of the occurrences and severity of potential abnormal events and
product-quality defects. A multivariate Bayesian analysis framework is
formulated for data analysis and predictive modeling.
These predictions provide direction and emphasis to improve safety and
product-quality performance.  As a case study,
an acrylic fed-batch polymerization reactor is considered to show the
application and reliability of this approach.

References

1. Meel, A., W. D. Seider, and U.
Oktem, ?Analysis of Management Actions, Human Behavior, and Process Reliability
in Chemical Plants.  I. Impact of Management Actions,? Proc. Safety Prog.,
27, 1, 7-14 (2008).

2. 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).

3. Meel, A., and W. D. Seider,
?Plant-Specific Dynamic Failure Assessment using Bayesian Theory,? Chem.
Eng. Sci., 61, 7036-7056 (2006).