FEP Analysis and Markov Chains
| Autor: | P. David, Ferhat Yavuz, M. Nepveu |
|---|---|
| Přispěvatelé: | TNO Bouw en Ondergrond |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Risk analysis
Engineering FEP Analysis Risk perception Operations research Initial state Process (engineering) Markov process Discrete time symbols.namesake Energy(all) Policy makers Machinery Risk management Risk assessment Probabilistic risk assessment Markov chain Probabilistic Risk Analysis business.industry Markov processes Probabilistic logic Chains Markov Chain Markov Chains Carbon storage Licensing authority Discrete time and continuous time Risk factors symbols Geological systems Safety factor business Hazardous process Physical process Supporting tool Geosciences |
| Zdroj: | Energy Procedia, 1, 1, 2519-2523 |
| ISSN: | 2519-2523 |
| Popis: | Uncertainties related with underground CO2 storage play a vital role in risk assessment with respect to Carbon storage and capture projects (CCS). The main purpose of risk assessment is to determine a qualitative and quantitative picture of hazardous processes or events. One makes a comprehensive inventory of risk factors, and stores the results in a FEP (Features, Events and Process) database. The properties of the geological system itself and natural or human -induced processes determine the future system p roperties. The FEP's may interact. In this paper we propose to describe this interaction of FEPs within the framework of (discrete time) Markov Chains. In such an approach various states are defined. The system can "jump" from one state to another. The probabilistic evolution of the system can be followed, and conclusions can be drawn as to visit times of the various states. Also, the most likely ultimate fate of a system in dependence of initial state can be determined. This approach offers a comple mentary supporting tool for scenario-thinking, as it takes into account the evolution of all possible follow -ups of relevant physical processes and events quantitatively. It is not about just following a few scenario's. Without the machinery of Markov Chains this can hardly be done in full. An added bonus of this approach is that questions of policy makers, and of licensing authorities can be answered in a numerical way. © 2009 Elsevier Ltd. All rights reserved. |
| Databáze: | OpenAIRE |
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