A combined distributed moving horizon estimation and distributed model predictive control architecture is proposed to address the distributed output−feedback control problem for nonlinear process systems. Community detection based on modularity maximization is used to generate separate optimal decompositions for the estimation and control problems on the basis of suitable graphs. The process of benzene alkylation with ethylene is used as a case study to illustrate the application and computational advantages of the proposed control strategy.

This paper addresses the plant-wide control of the amine gas sweetening plant using distributed model predictive control. The plant is fed natural gas containing sour gases (hydrogen sulfide and carbon dioxide), which are removed by absorption in monoethanolamine solution. A plant decomposition algorithm based on modularity maximization for distributed parameter systems is used to obtain the optimal decomposition for distributed model predictive control. Comparisons are drawn among the performance and computational requirements of distributed, decentralized, and centralized model predictive controls.

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