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在制定生物质能生产计划时,影响决策者的因素不仅包括生物质供应链本身的不同环节,而且包括生物质系统对于社会、环境和经济的影响及其在某个特定的国家内开发的困难程度。为了综合考虑上述因素,本文提出生物质能两层通用决策系统(gBEDS),其核心是数据库,包括基本的生物质信息和详细的决策信息,此外,还包括方案数据库和为新用户提供示范的案例库。在数据库的基础上,决策系统包括单元过程(UP)的模拟模块和用于优化决策的遗传算法。在图形界面的帮助下,用户可以自行定义生物质供应链,并进行环境、经济、社会或其他方面的评价;在生物质能生产全生命周期的模拟和优化模型的基础上,系统采用数据挖掘方法(模糊c均值聚类和决策树)确定最优的生物质原材料收集存储和转化工厂的地理位置。使用Matlab开发生物质供应链的生物质计划参数(例如费用和CO_2排放)的计算模型。同时,用地理信息系统(GIS)对生物能转化工厂和存储数据作可视化表达,以支持用户在智能输出的基础上做出决策。因此,gBEDS支持生物质能国家计划者,制定一种有效的生物质能生产计划并作出综合评价,地方的设计和实施者确定优化、详细的单元过程实施上述计划。日本森林废物发电的实例研究表明了上述方法的有效性和可行性。
The factors influencing decision makers in developing a biomass energy production plan include not only the different aspects of the biomass supply chain itself but also the social, environmental and economic impacts of the biomass system and the difficulties it has developed in a particular country degree. In order to comprehensively consider the above factors, this paper proposes a two-layer biomass generalized decision-making system (gBEDS), the core of which is the database, including basic biomass information and detailed decision-making information. In addition, it also includes the program database and provides demonstration for new users Case Library. Based on the database, the decision system includes the simulation module of the unit process (UP) and the genetic algorithm used to optimize the decision. With the help of the graphical interface, users can define their own biomass supply chain and make environmental, economic, social or other evaluations. Based on the simulation and optimization model of the whole life cycle of biomass energy production, the system adopts data mining Methods (fuzzy c-means clustering and decision trees) determine the optimal geographic location for the biomass raw material collection, storage and conversion plant. A computational model to develop biomass plan parameters (eg, costs and CO 2 emissions) for the biomass supply chain using Matlab. At the same time, GIS is used to visualize bio-energy conversion plants and storage data to support users in making decisions based on smart outputs. As a result, gBEDS supports biomass energy planners in developing an effective biomass production plan and making a comprehensive assessment, and local design and implementers identify optimized and detailed unit processes to implement the plan. A case study of Japanese forest waste generation shows the effectiveness and feasibility of the above method.