Valuable information about unsafe conditions resides in the large alarm databases of distributed control systems and emergency shutdown systems. This overlooked and underutilized information can be analyzed to identify process near-misses and determine the probability of serious accidents.
This article introduces the concept of dynamic risk analysis (DRA) based on alarm databases. It provides a general overview of what this is, how it can be used in chemical processing to improve safety, and challenges that must be addressed over the next 5–10 years. It also highlights current research in this area and offers perspective on methodologies most likely to succeed.
Ankur Pariyani is a 4th year PhD Candidate in Chemical Engineering at the University of Pennsylvania. In his doctoral research with Dr. Warren Seider, he is working on developing new risk analysis methods for large-scale chemical processes by utilizing their vast alarm databases. More recently, he is developing predictive techniques (or leading indicators) to alert the operators several hours in advance of undesirable safety and quality incidents. His other research interests include alarm management, process modeling and simulation and Bayesian statistics. He received his Bachelors in...Read more
Warren D. Seider is Professor of Chemical and Biomolecular Engineering at the University of Pennsylvania. He received a B.S. degree from the Polytechnic Institute of Brooklyn, and M.S. and Ph.D. degrees from the University of Michigan. For many years,he has contributed to the fields of process analysis, simulation, design, and control. In process design, he coauthored FLOWTRAN Simulation - An Introduction (with J.D. Seader and A.C. Pauls) and Product and Process Design Principles: Synthesis,...Read more
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