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Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection
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DOI：10.1021/acs.iecr.9b00820.png)
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Abstract
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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.
Keywords:
PROCESS NETWORKS
SUBSYSTEM DECOMPOSITION
BENZENE ALKYLATION
INTEGRATED PROCESS
STATE ESTIMATION
ARCHITECTURES
OPTIMIZATION
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