Design of Control Configurations for Complex Process Networks

Abstract

Tight integration is the rule rather than the exception in chemical and energy plants. Despite the significant economic benefits which result from efficient utilization of energy/material resources, effective control of plants with such integration becomes challenging; the network-level dynamics emerging from process interconnections and the model complexity of such plants limit the effectiveness of decentralized control approaches traditionally followed in plant-wide control. The development of effective control methods for complex integrated plants is a challenging, open problem. This thesis proposes methods to develop effective control strategies for two classes of process networks. In the first part of the thesis, a class of process networks, in which slow network-level dynamics is induced by large rates of energy and/or material recycle, is considered. A graph theoretic algorithm is developed for such complex material integrated process networks to i) identify the material balance variables evolving in each time scale, and ii) design hierarchical control structures by classifying potential manipulated inputs and controlled outputs in each time scale. The application of a similar algorithm developed for energy integrated networks to representative chemical processes is also presented. The second part of the thesis focuses on generic process networks where tight integration is not necessarily reflected on a segregation of energy and/or material flows. A method is developed to systematically synthesize control configurations with favorable structural coupling, using relative degree as a measure of such coupling. Hierarchical clustering methods are employed to generate a hierarchy of control configurations ranging from fully decentralized ones to a fully centralized one. An agglomerative hierarchical clustering method is first developed, in which groups of inputs/outputs are merged successively to form fewer and larger groups that are strongly connected topologically. Then, a divisive hierarchical clustering method is developed, in which groups of inputs/outputs are decomposed recursively into smaller groups. The developed methods are applied to typical chemical process networks.