This paper addresses the impact of decomposition on the closed-loop performance and computational efficiency of model predictive control (MPC) of nonlinear process networks. Distributed MPC structures with different communication strategies are designed for regulation of an integrated reactor-separator process. Different system decompositions are also considered, including decompositions into local controllers with minimum interactions obtained via community detection methods. The closed-loop performance and computational effort of the different MPC designs are analyzed. Through such a comprehensive comparison, tradeoffs between performance and computation effort, and the importance of systematic choice of the system decomposition, are documented. (Graph Presented).
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Financial support from the Petroleum Institute, Abu Dhabi, UAE is gratefully acknowledged.
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