(687g) Distributed Model Predictive Control of Integrated Process Networks Using Community Detection
AIChE Annual Meeting
2020 Virtual AIChE Annual Meeting
Computing and Systems Technology Division
Optimization-based Estimation and Control
Wednesday, November 18, 2020 - 9:30am to 9:45am
The system decomposition lies at the heart of DMPC design. It identifies the number of controllers and describes how the manipulated inputs and controlled output variables must be distributed across the control system. The closed-loop performance and the computational efficiency of the DMPC are impacted directly by a selected decomposition method that alters the geometry of subsystems and the communication between them .
Community detection on the representing graphs of integrated process systems has been shown a promising approach to synthesize distributed estimation and control architectures [4, 5]. In previous work, a community detection-based DMPC architecture was developed by maximizing the modularity of the representing directed graphs [6, 7]. Such a decomposition method results in a minimum inter-subsystem communication without considering the strength of interactions between variables belonging to different subsystems .
In this work, we explore the impact of variablesâ interactions on the community detection-based DMPC of complex process networks. Specifically, decomposition of the control problem into distributed sub-controllers is pursued to maximize the strength of input-output impact. To this end, the control problem is recast as a weighted directed graph and subsequently decomposed using community detection algorithm. As the strength of variable interaction is dependent on the operating state (e.g. recycle ratio), the corresponding optimal decomposition changes based on the operating state. We synthesize an iterative DMPC for output regulation of based on such distributed architecture and we evaluate the closed-loop performance and the computation time of the proposed DMPC for a benchmark reactor-separator process network over a wide operating range. It is illustrated that, for each operating point, the optimal decomposition maximizing the input-output impact performs better compared to ad-hoc architectures like topology-based or balance variable-based decompositions.
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