期刊
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
卷 116, 期 -, 页码 312-323出版社
INST CHEMICAL ENGINEERS
DOI: 10.1016/j.psep.2018.01.013
关键词
Dynamic failure prediction; Loss functions; Economic consequences; Process safety; Structure learning of Bayesian network from data and risk analysis
资金
- Natural Science and Engineering Council of Canada
- Canada Research Chair (CRC, Tier I) Program
This paper proposes a dynamic economic risk analysis methodology for process systems. The Bayesian Tree Augmented Naive Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation. The modified inverted normal loss function is used to define system economic losses as a function of process deviation. The time dependent probability of system deviation owing to an abnormal event is constantly updated based on the present state of the relevant process variables. The integration of real time probability of system deviation with potential losses provides the risk profile of the system at any instant. This risk profile can be used as the basis for operational decision making and also to activate the emergency safety system. The proposed methodology is tested and verified using the Richmond refinery accident. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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