A risk-based criticality analysis in bioenergy parks using P-graph method

Added Title

International Congress of Chemical and Process Engineering (22nd : 2016)
CHISA 2016
19th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (19th : 2016)
PRES 2016

College

Gokongwei College of Engineering

Department/Unit

Chemical Engineering

Document Type

Conference Proceeding

Source Title

22nd International Congress of Chemical and Process Engineering, CHISA 2016 and 19th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction, PRES 2016

Volume

2

First Page

929

Last Page

930

Publication Date

1-1-2016

Abstract

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.

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Disciplines

Chemical Engineering

Keywords

Biomass energy, Energy parks

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