Clustering methods for control-relevant decomposition of complex process networks

用于复杂过程网络的控制相关分解的聚类方法

• 批准号: 1605549
• 负责人: Prodromos Daoutidis
• 金额: $ 32万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1605549&HistoricalAwards=false
• 关键词: Clustering; methods; control; relevant; decomposition

1605549 PI: DaoutidisTitle: Clustering methods for control-relevant decomposition of complex process networksComplex process networks, consisting of interconnections of numerous reaction, separation and heat exchange units, are very common in modern chemical and energy plants. Effectively controlling such networks is a challenging problem that requires the development of distributed control strategies. In order to develop such strategies, constituent sub-networks must be identified that can be effectively controlled and coordinated within the overall process network. However, a rigorous control-oriented network decomposition framework that can be automated and applied to large-scale networks is currently lacking. The main goals of the proposed research are: i) to develop generalized methods for control-relevant decomposition of complex process networks, and (ii) to apply these methods to representative systems from the process and energy industries.The proposed project will employ hierarchical clustering methods that have been used extensively in network theory as a powerful framework for analyzing the inter-connectivity of complex process networks, and designing control structures for them. Within this framework, fundamental methods for control-relevant decomposition of integrated process networks will be developed. These methods will enable a systematic classification of fully-resolved input/output cluster hierarchies, ranging from a single cluster to individual collections of input/output pairs. They will also determine the optimal modularity of such clusters on the basis of appropriate measures of compactness and closeness. The developed network decomposition methods will therefore facilitate the transition from a fully decentralized control paradigm towards a more effective distributed paradigm for complex process networks. Such efficient plant-wide control strategies are critical to the economic viability, as well as the energy and environmental sustainability of chemical and energy industries. The proposed research will provide a setting for the effective training of graduate students in fundamental research, cutting across mathematics and control theory, with a timely and important application component. The research results will be broadly disseminated through publications and presentations, whereas the open-source software that will be developed will further enhance the infrastructure for research and education.

1605549 PI：Daoutidis标题：复杂过程网络的控制相关分解的聚类方法复杂过程网络由许多反应、分离和热交换单元相互连接而成，在现代化工和能源工厂中非常常见。有效地控制这样的网络是一个具有挑战性的问题，需要分布式控制策略的发展。为了制定这样的策略，必须确定在整个过程网络中可以有效控制和协调的组成子网络。然而，目前缺乏一个严格的面向控制的网络分解框架，可以自动化和应用于大规模网络。拟议研究的主要目标是：i）开发复杂过程网络的控制相关分解的通用方法，以及（ii）将这些方法应用于过程和能源行业的代表性系统。拟议项目将采用在网络理论中广泛使用的层次聚类方法作为分析复杂过程网络互连性的强大框架，并为它们设计控制结构。在此框架内，控制相关的集成过程网络的分解的基本方法将被开发。这些方法将能够对完全解析的输入/输出集群层次结构进行系统分类，范围从单个集群到输入/输出对的单独集合。他们还将根据适当的紧凑性和紧密性衡量标准，确定这些集群的最佳模块化。因此，开发的网络分解方法将促进从一个完全分散的控制模式过渡到一个更有效的分布式复杂过程网络的范例。这种有效的全厂控制策略对于化学和能源工业的经济可行性以及能源和环境可持续性至关重要。拟议中的研究将提供一个设置有效的培训研究生的基础研究，跨越数学和控制理论，及时和重要的应用组件。研究成果将通过出版物和演示文稿广泛传播，而将要开发的开放源码软件将进一步加强研究和教育的基础设施。