Book chapter
Improving Process Safety and Product Quality using Large Databases
Computer Aided Chemical Engineering, pp 175-180
2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper introduces a novel modeling and statistical framework (based on Bayesian theory) that utilizes extensive distributed control system and emergency shutdown databases, to perform thorough risk and vulnerability assessment of chemical/petrochemical plants. Quality variables are utilized, in addition to safety (or process) variables, to enhance both process safety and product quality. To effectively achieve these objectives, new concepts of abnormal events and upset states are defined, which permit the identification of near-miss events from the databases. The databases for a fluid catalytic cracking unit at a major petroleum refinery are used to demonstrate the application and performance of the techniques introduced herein. The results show that with the novel utilization of near-miss data, one can perform robust risk calculations using both product-quality and safety data.
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Details
- Title
- Improving Process Safety and Product Quality using Large Databases
- Creators
- Ankur Pariyani - University of PennsylvaniaWarren Seider - University of PennsylvaniaUlku Oktem - University of PennsylvaniaMasoud Soroush - Drexel University
- Publication Details
- Computer Aided Chemical Engineering, pp 175-180
- Publisher
- Elsevier
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000287659500030
- Scopus ID
- 2-s2.0-77955197309
- Other Identifier
- 991019170499904721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Chemical
- Operations Research & Management Science