Subsystem decomposition of process networks for simultaneous distributed state estimation and control

Abstract

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach.State variables in bold cannot be estimated using the available measurements of the corresponding subsystem. "NA" means that no variable is assigned to the corresponding subsystem.

Distributed monitoring of the absorption column of a post‐combustion CO2 capture plant

“…First, we briefly review the community detection–based subsystem decomposition approach in the work of Yin and Liu that is to be used for decomposing the absorption column into subsystems. The main idea of community structure detection is to partition a large‐scale network into smaller communities in a way, such that the inner‐connection between different nodes within a community is made very strong while the links that connect different communities are relatively sparse .…”

“…It is seen that the objective of community structure detection is well aligned with that of subsystem decomposition for distributed state estimation/monitoring/control, which expects minimal interaction among different subsystems. Motivated by this observation, community detection has been taken advantage of to develop subsystem decomposition approaches for distributed control and simultaneous distributed estimation and control . In this work, we resort to our method proposed in the work of Yin and Liu to perform community‐based subsystem decomposition.…”

“…The method in the work of Yin and Liu requires that the number of subsystems is user‐specified and is known a priori. Without loss of generality, we consider that five local MHE‐based estimators will be designed for monitoring, and system should be decomposed into five subsystems accordingly.…”

“…In this work, we propose a distributed monitoring scheme for the absorption column used in a typical PCC plant. The process is decomposed into subsystems optimally using a community detection–based method in the work of Yin and Liu . First, a distributed moving horizon state estimation network is developed for the absorption column.…”

A cyber‐secure control‐detector architecture for nonlinear processes

“…Furthermore, feasible mitigation practices using control strategies after the occurrence of attacks have not yet been explored. In light of these gaps, the contributions of this work are as follows: (a) construction of data‐based machine‐learning detection algorithms which can effectively detect multiple classes of intelligent cyber‐attacks; (b) design of a robust control architecture to promptly contain and eliminate the impact of cyber‐attacks by reconfiguring the control system; and (c) application of the proposed detection and mitigation schemes to a benchmark multivariable nonlinear process example, which is a process example widely used in literature to test the performance of new control system designs . The remainder of this paper is organized as follows: notation and the class of nonlinear process systems considered are presented in Section 2; the cyber‐secure control architecture is formulated in Section 3; the design and detection mechanism of cyber‐attacks are presented in Section 4; and the application of the proposed methodology to a nonlinear chemical process network is presented in Section 5.…”