Distributed Model Predictive Control of Integrated Process Networks Based on an Adaptive Community Detection Approach

An adaptive community detection-based system decomposition approach is developed for distributed model predictive control (DMPC) of integrated process networks. In this approach, the weighted graph representation of the nonlinear state space model is utilized as the foundation for system decomposition. The most modular decomposition is identified for designing a distributed architecture by applying spectral community detection and energy-based separation techniques. The resulting decomposition evolves as the process network transitions through different operating conditions. Consequently, the distributed architecture and the DMPC design are adjusted to improve closed-loop performance and robustness. To illustrate the efficacy of the proposed approach, a benzene alkylation process benchmark under different operating conditions is used. The results of the simulation studies demonstrate that the decompositions derived through spectral community detection, based on the weighted graph representations, lead to enhanced closed-loop performance and improved computational efficiency compared to the commonly used unweighted hierarchical community-based decompositions.