Clustering methods for control-relevant decomposition of complex process networks

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

1605549 PI: Daoutidis Title: Clustering methods for control-relevant decomposition of complex process networks Complex 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.