Clustering methods for control-relevant decomposition of complex process networks

题目：复杂过程网络控制相关分解的聚类方法复杂过程网络由众多反应、分离和换热单元的相互连接组成，在现代化工和能源装置中非常常见。有效控制此类网络是一个具有挑战性的问题，需要开发分布式控制策略。为了制定这种战略，必须确定能够在整个过程网络内有效控制和协调的组成子网络。然而，目前缺乏一种能够自动化并应用于大规模网络的严格的面向控制的网络分解框架。所提出的研究的主要目标是：i)开发复杂过程网络的控制相关分解的广义方法，以及（ii）将这些方法应用于过程和能源工业的代表性系统。该项目将采用网络理论中广泛使用的分层聚类方法，作为分析复杂过程网络互连性的强大框架，并为其设计控制结构。在这个框架内，将开发与控制有关的集成过程网络分解的基本方法。这些方法将支持对完全解析的输入/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措施确定这些集群的最佳模块性。因此，开发的网络分解方法将促进从完全分散的控制范式向复杂过程网络更有效的分布式范式的过渡。这种高效的全厂控制策略对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。所建议的研究将为研究生在基础研究方面的有效培训提供一个环境，跨越数学和控制理论，具有及时和重要的应用组成部分。研究结果将通过出版物和简报广泛传播，而将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。

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.