College
Gokongwei College of Engineering
Department/Unit
Chemical Engineering
Document Type
Article
Source Title
Chemical Engineering Transactions
Volume
56
First Page
475
Last Page
480
Publication Date
1-1-2017
Abstract
Bioenergy parks are low carbon and production-efficient integrated networks, but are inherently vulnerable to cascading failures due to capacity disruptions (i.e. reduction in production levels). The reduction in production levels may be attributed to climate change-induced disruptions such as drought that results in lower supply of biofuel feedstocks. The extent of damage within a bioenergy park is dependent on network topology as well as magnitude of disruption. Thus, it is imperative for such systems to be designed properly in order to tolerate multiple disruption scenarios. Although individual bioenergy plants are designed to have minimum partial load or maximum rated capacities, such safety factor maybe trivial given that the effect of perturbations in highly integrated networks are either dampened or amplified. Thus, it is important to assess the sensitivity of a bioenergy park to fluctuations in the production levels of component plants. In this work, a Monte Carlo simulation approach is proposed to assess the vulnerability of bioenergy parks to variable capacity disruptions. Results show that the reliability of the final output is dependent on the component plant's connectivity within the network and highly-connected bioenergy plants results in high probability of failure. A case study on determining the robustness of the given network configuration is used to illustrate the methodology. Copyright © 2017, AIDIC Servizi S.r.l..
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Digitial Object Identifier (DOI)
10.3303/CET1756080
Recommended Citation
Benjamin, M. D., Tan, R. R., & Razon, L. F. (2017). Assessing the sensitivity of bioenergy parks to capacity disruptions using Monte Carlo simulation. Chemical Engineering Transactions, 56, 475-480. https://doi.org/10.3303/CET1756080
Disciplines
Chemical Engineering
Keywords
Energy parks; Monte Carlo method
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