Development of an integrated decision support system for supporting offshore oil spill response in harsh environments
Offshore oil spills can lead to significantly negative impacts on socio-economy and constitute a direct hazard to the marine environment and human health. The response to an oil spill usually consists of a series of dynamic, time-sensitive, multi-faceted and complex processes subject to various cons...
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| Format: | Thesis |
| Language: | English |
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Memorial University of Newfoundland
2014
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| Online Access: | https://research.library.mun.ca/6498/ https://research.library.mun.ca/6498/1/PhD_Thesis_Final_Pu_Li.pdf https://research.library.mun.ca/6498/3/PhD_Thesis_Final_Pu_Li.pdf |
| Summary: | Offshore oil spills can lead to significantly negative impacts on socio-economy and constitute a direct hazard to the marine environment and human health. The response to an oil spill usually consists of a series of dynamic, time-sensitive, multi-faceted and complex processes subject to various constraints and challenges. In the past decades, many models have been developed mainly focusing on individual processes including oil weathering simulation, impact assessment, and clean-up optimization. However, to date, research on integration of offshore oil spill vulnerability analysis, process simulation and operation optimization is still lacking. Such deficiency could be more influential in harsh environments. It becomes noticeably critical and urgent to develop new methodologies and improve technical capacities of offshore oil spill responses. Therefore, this proposed research aims at developing an integrated decision support system for supporting offshore oil spill responses especially in harsh environments (DSS-OSRH). Such a DSS consists of offshore oil spill vulnerability analysis, response technologies screening, and simulation-optimization coupling. The uncertainties and/or dynamics have been quantitatively reflected throughout the modeling processes. First, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) approach has been developed. A real-world case study was applied for offshore oil spill vulnerability index (OSVI) classification in the south coast of Newfoundland to demonstrate this approach. Furthermore, a Monte Carlo simulation based integrated rule-based fuzzy adaptive resonance theory mapping (MC-IRFAM) approach has been developed for screening and ranking for spill response and clean-up technologies. The feasibility of the MC-IRFAM was tested with a case of screening and ranking response technologies in an offshore oil spill event. A novel Monte Carlo simulation based dynamic mixed integer nonlinear programming (MC-DMINP) approach has also been developed for the ... |
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