Development of a risk analysis methodology for symbiotic bioenergy parks

• Main Author: Benjamin, Michael Francis D.
• Format: text
• Language: English
• Published: Animo Repository 2015
• Online Access: https://animorepository.dlsu.edu.ph/etd_doctoral/430
• Institution: De La Salle University

The utilization of biofuels is one of the potential pathways in meeting the worlds increasing energy demands as well as in reducing global greenhouse gas emissions. Although biofuels are renewable resources and their production is viewed as nearly carbon neutral, there are sustainability issues raised in their use (i.e., water, land, carbon, and nitrogen footprint). The creation of bioenergy parks is a prospective solution in addressing these critical environmental issues. A bioenergy park is an industrial symbiosis (IS) network developed based on material and energy synergies among bioenergy and auxiliary plants to increase efficiency and reduce carbon emissions. However, the resulting highly-integrated system exhibits strong interdependence among components, thus resulting in increased vulnerability to capacity disruptions (i.e., plant inoperability) because of failure propagation. Such risks are a potential barrier to the implementation of bioenergy parks despite their advantages. In this dissertation, a novel framework is developed for analyzing the risks and resilience of bioenergy parks. First, a criticality index is developed to identify the most vital component plant within a bioenergy park based on principles of input-output (I-O) analysis. In this work, criticality is defined as the measure to which a disrupted component plant is the source of network vulnerability. A probabilistic multi-disruption risk index is then developed to quantitatively assess the vulnerability of the entire bioenergy park against an array of anticipated risk scenarios using I-O and analytic hierarchy process (AHP). Finally, a disruption resilience framework is developed to analyze the recovery of disrupted component plants in a bioenergy park using the concepts derived from dynamic inoperability input-output modeling (DIIM). This work thus contributes to the systematic analysis of risks associated with bioenergy parks, which is an underdeveloped research area. These risk-based models and frameworks can be used by the government and IS network planners for creating polices as well as risk management strategies, respectively, to develop robust and resilient bioenergy parks.