Graph-theoretic methods for reduction and control of complex process networks

用于简化和控制复杂过程网络的图论方法

• 批准号: 1133167
• 负责人: Prodromos Daoutidis
• 金额: $ 31.8万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1133167&HistoricalAwards=false
• 关键词: Graph; theoretic; methods; reduction; control

Proposal Number: 1133167PI: Daoutidis, Prodromos Complex process networks, consisting of interconnections of numerous reaction, separation and heat exchange units, represent a key feature of modern chemical and energy plants. Controlling such networks effectively, especially in the context of transitions between different steady states, is a challenging problem. The intricate dynamics and model complexity that such networks exhibit, make conventional decentralized control design inadequate and fully centralized control design approaches impractical. Despite some progress in analyzing simple networks and designing distributed or hierarchical controllers for such networks, a rigorous yet scalable model simplification and controller design framework for complex networks is currently lacking. The goals of the this research are therefore: (i) to develop scalable methods for model reduction and decomposition of complex process networks, (ii) to develop concrete control algorithms for transition control of such networks, and (iii) to apply the developed methods to representative systems from the process and energy industries, including complex thermally coupled distillation trains, cryogenic systems and integrated reformer - fuel cell systems.Intellectual Merit: The main novelty of this work is the introduction of graph theory as a framework for analyzing the structural properties of such complex networks, responsible for their ensemble behavior. Specifically, their modular structure lends itself to a graph theoretic analysis, whereby weak and strong connections between process units arising from time scale separation or other connectivity properties can be identified from structural information; these can be used either for model reduction (when a reduced model does exist, e.g. owing to a slow, low-order network dynamics) or model decomposition in the more general case where the ensemble behavior is captured through the aggregate behavior of some distinct, loosely coupled community structures within the network. For both cases, scalability to large scale networks will be enabled by using powerful graph-theoretic algorithms for automating the model simplification and possibly the controller design procedure. A software tool that will achieve this will be built within an object oriented programming framework.Broader Impact: Controlling complex, integrated plants is a critical link to the economic viability, and the energy and environmental sustainability of the chemical and energy supply chains. This research will develop computational tools that will enable the development of such control methods in an automated and scalable fashion. This analysis framework can also be applied to complex networks from other disciplines, such as complex reaction pathways, ecological networks and social networks. The research will provide a setting for the effective training of graduate students in fundamental research cutting across mathematics and control, with a timely and important application component. The students will also interact with industrial partners through summer internships. The research results will be broadly disseminated through publications and presentations, and through their integration into the teaching of process control. The software that will be developed will further enhance the infrastructure for research and education.

提案编号：1133167 PI：复杂的过程网络由众多反应、分离和热交换单元相互连接而成，是现代化工和能源工厂的一个重要特征。有效地控制这样的网络，特别是在不同的稳态之间的过渡的背景下，是一个具有挑战性的问题。 这种网络表现出的复杂动态和模型复杂性使得传统的分散控制设计不足，并且完全集中的控制设计方法不切实际。 尽管在分析简单网络和设计用于此类网络的分布式或分层控制器方面取得了一些进展，但目前缺乏用于复杂网络的严格但可扩展的模型简化和控制器设计框架。因此，本研究的目标是：（i）开发用于复杂过程网络的模型简化和分解的可扩展方法，（ii）开发用于这种网络的过渡控制的具体控制算法，以及（iii）将所开发的方法应用于来自过程和能源工业的代表性系统，包括复杂的热耦合蒸馏列，低温系统和集成重整器-燃料电池系统。智力优点：这项工作的主要新奇是引入图论作为分析这种复杂网络的结构特性的框架，负责它们的系综行为。具体地，它们的模块化结构适合于图论分析，由此可以从结构信息识别由时间尺度分离或其它连接特性引起的处理单元之间的弱连接和强连接;这些可以用于模型简化，（当简化模型确实存在时，例如由于缓慢，低阶网络动力学）或更一般情况下的模型分解，其中通过网络内的一些不同的、松散耦合的社区结构的聚集行为来捕获总体行为。对于这两种情况，大规模网络的可扩展性将通过使用强大的图论算法来自动化模型简化和可能的控制器设计过程来实现。更广泛的影响：控制复杂的综合工厂是化学和能源供应链的经济可行性、能源和环境可持续性的关键环节。这项研究将开发计算工具，使这种控制方法的发展在一个自动化和可扩展的方式。该分析框架也可以应用于其他学科的复杂网络，如复杂反应途径、生态网络和社交网络。这项研究将提供一个设置有效的培训研究生在基础研究跨越数学和控制，及时和重要的应用组件。学生还将通过暑期实习与工业合作伙伴互动。研究成果将通过出版物和演示文稿，并通过其融入过程控制的教学中广泛传播。 即将开发的软件将进一步加强研究和教育的基础设施。