Analyzing the disruption resilience of bioenergy parks using dynamic inoperability input–output modeling

被引:14
作者
Benjamin M.F.D. [1 ,3 ]
Ubando A.T. [2 ]
Razon L.F. [1 ]
Tan R.R. [1 ]
机构
[1] Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, Manila
[2] Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, Manila
[3] Chemical Engineering Department, University of Santo Tomas, España Blvd., Manila
关键词
Disruption resilience; Dynamic inoperability input–output model; Industrial symbiosis; Microalgal multi-functional bioenergy system; Recovery rates;
D O I
10.1007/s10669-015-9562-5
中图分类号
学科分类号
摘要
Bioenergy parks are low-carbon industrial symbiosis networks that are comprised of biomass processing plants. However, such highly integrated energy systems are inherently vulnerable to capacity disruptions. The strong interdependencies among component plants in a bioenergy park decrease system resilience due to cascading failure effect. The consequences of such disruptions are even greater if the critical components are damaged. Resilience is defined as the ability of an energy system to withstand a disruption and subsequently recover to its normal state. In this work, a disruption resilience framework is developed to analyze the resilience of bioenergy parks against an array of capacity disruption scenarios. This framework is derived from dynamic inoperability input–output modeling previously used in economic and critical infrastructure systems. A microalgal multi-functional bioenergy system case study is presented to demonstrate the applicability of the resilience framework. The example shows that the resilience of a bioenergy park is influenced by both the recovery time of component plants and their degree of connectivity within the network; such insights can be used for planning more disruption-resilient bioenergy parks. © 2015, Springer Science+Business Media New York.
引用
收藏
页码:351 / 362
页数:11
相关论文
共 42 条
[1]  
Akhtar R., Santos J.R., Risk-based input–output analysis of hurricane impacts on interdependent regional workforce systems, Nat Hazards, 65, pp. 391-405, (2013)
[2]  
Aven T., On some recent definitions and analysis frameworks for risk, vulnerability, and resilience, Risk Anal, 31, pp. 515-522, (2011)
[3]  
Barker K., Santos J.R., Measuring the efficacy of inventory with a dynamic input–output model, Int J Prod Econ, 126, pp. 130-143, (2010)
[4]  
Benjamin M.F., Tan R.R., Razon L.F., A methodology for criticality analysis in integrated energy systems, Clean Technol Environ Policy, 17, pp. 935-946, (2015)
[5]  
Benjamin M.F., Tan R.R., Razon L.F., Probabilistic multi-disruption risk analysis in bioenergy parks via physical input–output modeling and analytic hierarchy process, Sustainable Prod Consum, 1, pp. 22-33, (2015)
[6]  
Bruneau M., Chang S.E., Eguchi R.T., Lee G.C., O'Rourke T.D., Reinhorn A.M., Shinozuka M., Tierney K., Wallace W.A., von Winterfeldt D., A framework to quantitatively assess and enhance the seismic resilience of communities, Earthq Spectra, 19, pp. 733-752, (2003)
[7]  
Chertow M.R., Industrial symbiosis: literature and taxonomy, Annu Rev Energy Environ, 25, pp. 331-337, (2000)
[8]  
Chertow M.R., Uncovering industrial symbiosis, J Ind Ecol, 11, pp. 11-30, (2007)
[9]  
Chopra S.S., Khanna V., Understanding resilience in industrial symbiosis networks: insights from network analysis, J Environ Manag, 141, pp. 86-94, (2014)
[10]  
Folke C., Resilience: the emergence of a perspective for social-ecological systems analysis, Glob Environ Change, 16, pp. 253-267, (2006)