TY - JOUR
T1 - A hybrid approach to prioritize risk mitigation strategies for biomass polygeneration systems
AU - Ngan, Sue Lin
AU - How, Bing Shen
AU - Teng, Sin Yong
AU - Leong, Wei Dong
AU - Loy, Adrian Chun Minh
AU - Yatim, Puan
AU - Promentilla, Michael Angelo B.
AU - Lam, Hon Loong
N1 - Funding Information:
The authors would like to thank Long Term Research Grant Scheme [LRGS/2013/UKM-UKM/PT/06] from Ministry of Higher Education (MOHE), Malaysia, EP-2017-028 under the leadership of Prof. Dr. Er Ah Choy, Universiti Kebangsaan Malaysia, University of Nottingham Malaysia Campus, Newton Fund, the EPSRC/RCUK (Grant Number: EP/PO18165/1) and Research Micro Fund from Swinburne University of Technology (Sarawak Campus) [Grant Number: 9?1372 RMFST] for the funding of this research. The research leading to these results has also received funding from the Ministry of Education, Youth and Sports of the Czech Republic under OP RDE grant number CZ.02.1.01/0.0/0.0/16_026/0008413 ?Strategic Partnership for Environmental Technologies and Energy Production?.
Funding Information:
The authors would like to thank Long Term Research Grant Scheme [LRGS/2013/UKM-UKM/PT/06] from Ministry of Higher Education (MOHE), Malaysia, EP-2017-028 under the leadership of Prof. Dr. Er Ah Choy, Universiti Kebangsaan Malaysia , University of Nottingham Malaysia Campus , Newton Fund, the EPSRC / RCUK (Grant Number: EP/PO18165/1) and Research Micro Fund from Swinburne University of Technology (Sarawak Campus) [Grant Number: 9–1372 RMFST ] for the funding of this research. The research leading to these results has also received funding from the Ministry of Education, Youth and Sports of the Czech Republic under OP RDE grant number CZ.02.1.01/0.0/0.0/16_026/0008413 “Strategic Partnership for Environmental Technologies and Energy Production”. Appendix Detailed results of Monte Carlo simulations for each action plan are shown in figures below ( Figure A1 , Figure A2 , Figure A3 , Figure A4 , Figure A5 , Figure A6 , Figure A7 , Figure A8 , Figure A9 , Figure A10 , Figure A11 , Figure A12 , Figure A13 , Figure A14 , Figure A15 , Figure A16 . Fig. A1 PBP frequency distribution of base case. Fig. A1 Fig. A2 NPV frequency distribution of base case. Fig. A2 Fig. A3 PBP frequency distribution of AP1-HD. Fig. A3 Fig. A4 NPV frequency distribution of AP1-HD. Fig. A4 Fig. A5 PBP frequency distribution of AP2-HS. Fig. A5 Fig. A6 NPV frequency distribution of AP2-HS. Fig. A6 Fig. A7 PBP frequency distribution of AP3-FI. Fig. A7 Fig. A8 NPV frequency distribution of AP3-FI. Fig. A8 Fig. A9 PBP frequency distribution of AP4-SF. Fig. A9 Fig. A10 NPV frequency distribution of AP4-SF. Fig. A10 Fig. A11 PBP frequency distribution of AP4-SF. Fig. A11 Fig. A12 NPV frequency distribution of AP4-SF. Fig. A12 Fig. A13 PBP frequency distribution of AP6-RF. Fig. A13 Fig. A14 NPV frequency distribution of AP6-RF. Fig. A14 Fig. A15 PBP frequency distribution of AP7-CM. Fig. A15 Fig. A16 NPV frequency distribution of AP7-CM. Fig. A16
Publisher Copyright:
© 2019 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4
Y1 - 2020/4
N2 - Biomass polygeneration system is one of the most attractive biomass technologies due to its technicality, feasibility and high associated investment returns. The synthesis, design and economic aspects of constructing a processing system using this technology are well-developed and have recently reached the stage of industrial implementation. Nonetheless, the early stage of technology development focuses on process and product safety and tends to ignore other risk aspects that are closely associated with the biomass value chain. Due to the complex nature of the biomass value chain, conventional risk mitigation strategies are ineffective in mitigating risks at the management level. More recent approaches, particularly stochastic programming methods, have yielded robust results in addressing technological risks and design uncertainties. However, such approaches are still unable to effectively consider non-quantitative risks such as business risks and regulatory risks. Hence, this study proposes a combined method of an analytical model and stochastic programming approach to prioritize risks and risk mitigation strategies for decision-making purposes. This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method. The novel method functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders. As the stochastic risk mitigation counterpart, the fluctuations and uncertainties in operations, transportation, market supply-demand and price are modeled using the Monte Carlo simulation method. From this, risks of implementing biomass polygeneration systems can be mitigated by selecting a strategy that yields the highest analytical indicator while reconciling with the corresponding probabilities of achieving management goals. A palm biomass polygeneration system in Malaysia is presented as case study where the key implementation risks are regulatory risks, financing risks, technology risks, supply chain and feedstock risks, business risks, social and environmental risks.
AB - Biomass polygeneration system is one of the most attractive biomass technologies due to its technicality, feasibility and high associated investment returns. The synthesis, design and economic aspects of constructing a processing system using this technology are well-developed and have recently reached the stage of industrial implementation. Nonetheless, the early stage of technology development focuses on process and product safety and tends to ignore other risk aspects that are closely associated with the biomass value chain. Due to the complex nature of the biomass value chain, conventional risk mitigation strategies are ineffective in mitigating risks at the management level. More recent approaches, particularly stochastic programming methods, have yielded robust results in addressing technological risks and design uncertainties. However, such approaches are still unable to effectively consider non-quantitative risks such as business risks and regulatory risks. Hence, this study proposes a combined method of an analytical model and stochastic programming approach to prioritize risks and risk mitigation strategies for decision-making purposes. This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method. The novel method functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders. As the stochastic risk mitigation counterpart, the fluctuations and uncertainties in operations, transportation, market supply-demand and price are modeled using the Monte Carlo simulation method. From this, risks of implementing biomass polygeneration systems can be mitigated by selecting a strategy that yields the highest analytical indicator while reconciling with the corresponding probabilities of achieving management goals. A palm biomass polygeneration system in Malaysia is presented as case study where the key implementation risks are regulatory risks, financing risks, technology risks, supply chain and feedstock risks, business risks, social and environmental risks.
KW - Biomass polygeneration system
KW - Decision and evaluation-based fuzzy analytic network process (DEFANP)
KW - Monte Carlo simulation
KW - Multiple criteria decision making (MCDM)
KW - Risk management
UR - http://www.scopus.com/inward/record.url?scp=85077315115&partnerID=8YFLogxK
U2 - 10.1016/j.rser.2019.109679
DO - 10.1016/j.rser.2019.109679
M3 - Article
AN - SCOPUS:85077315115
SN - 1364-0321
VL - 121
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
M1 - 109679
ER -
