A hybrid approach to prioritize risk mitigation strategies for biomass polygeneration systems

• Main Authors: Ngan, Sue Lin, How, Bing Shen, Teng, Sin Yong, Leong, Wei Dong, Loy, Adrian Chun Minh, Yatim, Puan, Promentilla, Michael Angelo B., Lam, Hon Loong
• Format: text
• Published: Animo Repository 2020
• Subjects: Biomass energy; Polygeneration systems; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2295; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3294/type/native/viewcontent
• Institution: De La Salle University

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. © 2019 Elsevier Ltd