Case Reasoning-Based Emergency Decision Making for Oil and Gas Accidents
| Autor: | Zaixu Zhang, Ruifang La, Pengfei Bai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies 010504 meteorology & atmospheric sciences Operations research Computer science business.industry Fossil fuel 0211 other engineering and technologies Bayesian network 02 engineering and technology 01 natural sciences Safety Risk Reliability and Quality business Engineering (miscellaneous) 0105 earth and related environmental sciences |
| Zdroj: | Journal of Disaster Research. 15:981-990 |
| ISSN: | 1883-8030 1881-2473 |
| DOI: | 10.20965/jdr.2020.p0981 |
| Popis: | Throughout the digitization of the petrochemical industry, the Beidou technology-based disaster monitoring, evaluation, and early warning network system has supported emergency decision making for oil and gas accidents. Many problems arise throughout the emergency decision-making process during oil–gas accidents, such as the limited time for decision making, high complexity, and inadequate emergency plans. Targeting these issues, we propose the construction of a case library using a Bayesian network. This way, when a new accident occurs, its similarity and deviation indexes could be matched against those of historical cases registered in the database. As such, the candidate cases are adjusted using a case combination and pruning method, yielding the final qualified case model. A case verification of the “11.22” Sinopec Oil pipeline leak and explosion in Qingdao reveals that the proposed method only requires an oil and gas accident database to be built in advance, eliminating the need for sampling data to make decisions, and reducing the search space. Using the proposed case-based reasoning, historical data and experience regarding oil and gas emergency decisions can be activated and reused, which would greatly improve the modeling efficiency of the Bayesian network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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