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.

生物质多联产系统由于其技术性，可行性和高投资回报而成为最有吸引力的生物质技术之一。使用该技术构建处理系统的综合，设计和经济方面已经得到了很好的发展，并且最近已经进入工业实施阶段。尽管如此，技术开发的早期阶段集中在过程和产品安全上，并且倾向于忽略与生物质价值链密切相关的其他风险方面。由于生物质价值链的复杂性，传统的风险缓解策略在缓解管理层风险方面效果不佳。最近的方法，尤其是随机编程方法，在解决技术风险和设计不确定性方面取得了可观的结果。然而，这些方法仍无法有效地考虑非量化风险，例如业务风险和监管风险。因此，本研究提出了一种分析模型和随机规划方法相结合的方法，以便为决策制定优先级的风险和风险缓解策略。这项工作提出了一种基于模糊集理论的新型多准则决策专家系统，该系统是基于决策和评估的模糊分析网络过程（DEFANP）方法。该新方法的功能是在项目目标，生物质工业的关键组成部分和工业利益相关者的网络关系内确定风险缓解策略的优先级。作为降低随机风险的对口，操作，运输，使用蒙特卡洛模拟方法对市场供求和价格进行建模。由此，可以通过选择产生最高分析指标同时与实现管理目标的相应可能性相协调的策略来降低实施生物质多联产系统的风险。本文以马来西亚的棕榈生物质多联产系统为例进行了研究，其中主要的实施风险是监管风险，融资风险，技术风险，供应链和原料风险，商业风险，社会和环境风险。