基于全生命周期的低碳能源工业系统可持续性多角色多准则决策数学模型的构建和应用

The sustainability assessment and prioritization of industrial systems are always very challenging tasks due to the existence of: 1). interdependencies and interactions among the evaluation criteria; 2): both the hard criteria and soft criteria that are difficult to be quantitatively assessed;3). various types of uncertainties that are associated with the assessable information, possessed knowledge, and problem understanding, etc. In this project, we aim to develop various object–oriented multi-actor multi-criteria decision making methodologies for the sustainability assessment, analysis and enhancement of low-carbon energy industrial systems by combining the uncertainty theories like fuzzy interval theory, grey theory with the improved multi-criteria decision making methodologies. By using the developed methodologies, we can prioritize the criteria affecting the sustainable development of low-carbon energy industrial systems in China, quantify the effect of the various development strategies, and propose policy strategic measures to promote their sustainable development with a life cycle perspective.

可持续评价与优化对于工业系统的可持续性发展具有重要的意义，然而却面临诸多难题：(1). 可持续评价指标之间的相互作用和依赖关系；(2). 软指标和硬指标兼顾的问题；(3). 评价信息不确定和不完整的问题。此申请旨在通过结合区间理论、灰色理论、模糊理论等非确定性理论和改进的多准则决策方法发展各种面向对象的多角色多准则决策方法用于低碳能源工业系统的可持续性评价。利用发展的数学模型, 我们能够从全生命周期的角度量化识别影响能源工业过程可持续表现的关键因素，优化和设计可持续性强的能源工业系统和提出政策措施提高已有能源工业系统的可持续性。

本项目基于全生命周期的视角，构建多准则决策数学模型，用于低碳能源和化学工业复杂系统的可持续性评价和分析，构建的模型具有以下特征和优点：1). 将模糊三角数与主观赋权BWM方法以及客观赋权CRITIC相结合，实现混合信息下的主客观综合赋权。该方法允许包含众多利益相关者的群体参与决策过程，并使用模糊数字表达自己的意见和偏好；2). 发展了灰色关联度和不确定性Choquet积分偏好模型，处理评价规则之间的相互作用和关联。将构建的数学模型用于基于可再生能源的海水淡化、氨合成、二氧化碳捕捉及储存等过程的可持续性决策及分析, 从全生命周期的角度量化识别影响能源和化学工业过程可持续表现的关键因素，为相关工业系统的可持续性发展提供有意义的建议。