Journal article
Improved Predictions of Alarm and Safety System Performance Through Process and Operator Response-Time Modeling
AIChE journal, v 62(9), pp 3461-3472
01 Sep 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Dynamic risk analysis (DRA) has been used widely to analyze the performance of alarm and safety interlock systems of manufacturing processes. Because the most critical alarm and safety interlock systems are rarely activated, little or no data from these systems are often available to apply purely-statistical DRA methods. Moskowitz et al. (2015) 1 introduced a repeated-simulation, process-model-based technique for constructing informed prior distributions, generating low-variance posterior distributions for Bayesian analysis, 1 and making alarm-performance predictions. This article presents a method of quantifying process model quality, which impacts prior and posterior distributions used in Bayesian Analysis. The method uses higher-frequency alarm and process data to select the most relevant constitutive equations and assumptions. New data-based probabilistic models that describe important special-cause event occurrences and operators' response-times are proposed and validated with industrial plant data. These models can be used to improve estimates of failure probabilities for alarm and safety interlock systems. VC 2016 American Institute of Chemical Engineers
Metrics
Details
- Title
- Improved Predictions of Alarm and Safety System Performance Through Process and Operator Response-Time Modeling
- Creators
- Ian H. Moskowitz - Dept. of Chemical and Biomolecular EngineeringUniversity of PennsylvaniaPhiladelphia PA19104‐6393Warren D. Seider - Dept. of Chemical and Biomolecular EngineeringUniversity of PennsylvaniaPhiladelphia PA19104‐6393Jeffrey E. Arbogast - American Air LiquideNewark DE19702Ulku G. Oktem - Risk Management and Decision Processes Center, Wharton SchoolUniversity of PennsylvaniaPhiladelphia PA19104Ankur Pariyani - Near‐Miss Management. LLCPhiladelphia PA19104Masoud Soroush - Drexel University
- Publication Details
- AIChE journal, v 62(9), pp 3461-3472
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- CBET-1066475; CBET-1066461 / NSF; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000382987000037
- Scopus ID
- 2-s2.0-84987784591
- Other Identifier
- 991019168681004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Engineering, Chemical