A risk-based criticality analysis in bioenergy parks using P-graph method
The adoption of bioenergy parks is a prospective solution to increase the sustainability of stand-alone biomass processing plants. Production and resource efficiency, lower carbon emissions, and economic sustainability are achieved by synergistic exchanges of material and energy resources between co...
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| Main Authors: | , , , , |
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| Format: | text |
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Animo Repository
2016
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/280 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1279&context=faculty_research |
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| Institution: | De La Salle University |
| Summary: | The adoption of bioenergy parks is a prospective solution to increase the sustainability of stand-alone biomass processing plants. Production and resource efficiency, lower carbon emissions, and economic sustainability are achieved by synergistic exchanges of material and energy resources between components plants. However, such increased plant interdependency and the resulting integrated energy system is vulnerable to capacity disruptions. Cascading failure due to such disruptive event is an inherent risk in bioenergy parks and may pose as a barrier in implementing such system. The extent of risk originating from disrupted critical component plants in the network exhibited to be higher. A previous study developed a novel risk-based criticality index, based on input-output models, to quantify the effect of a component plant's disruption within a bioenergy park. This index is used to rank the plant's relative risk in the network based on its disruption consequence. In this work, a P-graph approach is proposed as an alternative methodology for criticality analysis of component plants in a bioenergy park. The P-graph framework is initially developed for solving process network synthesis, but recently being used to solve similarly structured problems. This risk-based metric can also be used for developing risk management measures to protect critical infrastructures, thereby increasing the robustness of bioenergy parks against disruptions. A case study is then presented to demonstrate the effectiveness of this method. Copyright © 2016, AIDIC Servizi S.r.l. |
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