Graph representation and distributed control of diffusion-convection-reaction system networks

Moharir, Manjiri; Pourkargar, Davood Babaei; Almansoori, Ali; Daoutidis, Pródromos

doi:10.1016/j.ces.2018.11.062

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…Such an optimal decomposition is also independent of the specific values of the system parameters and steady states since it only considers the model structure. The readers may refer to refs and for a step by step explanation of the modularity maximization algorithm.…”

Section: Decompositions For Control and Estimationmentioning

confidence: 99%

“…These interactions define the adjacency matrices that are used in the community detection algorithm. The readers may refer to refs and for a step-by-step explanation of the modularity maximization algorithm used.…”

Section: Distributed Estimation and Control Architecturementioning

confidence: 99%

“…In the section Model Predictive Control and Moving Horizon Estimation, we present the mathematical description of the studied class of nonlinear systems and review MPC and MHE synthesis for output regulation. In the Section Distributed Estimation and Control, we describe the community-based system decomposition for estimation and control, for which we adopt the algorithms applied in refs and and the distributed estimation/control architecture. Finally, the impact of system decomposition on the closed-loop performance and computational effort of distributed estimation and control is examined for the process of benzene alkylation with ethylene, emphasizing the importance of system decomposition in distributed output feedback control.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Pourkargar

Moharir

Almansoori

et al. 2019

Ind. Eng. Chem. Res.

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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.

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Section: Decompositions For Control and Estimationmentioning

confidence: 99%

Section: Distributed Estimation and Control Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Pourkargar

Moharir

Almansoori

et al. 2019

Ind. Eng. Chem. Res.

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“…Recently, Moharir et al 107,108 introduced a digraph representation for distributed parameter systems (control systems in partial differential equations), and applied the modularitybased community detection to the distributed model predictive control of a gas sweetening plant and a benchmark process with tubular reactors and flash separators. These works also demonstrated computational advantages of community-based decompositions.…”

Section: Modularity-based Decomposition For Distributed Control and Optimizationmentioning

confidence: 99%

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

2019

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“…The argument behind this measure is that modular organizations that arise in natural systems are nonrandom. This measure is intuitive and has seen many interesting applications; for instance, this measure has been shown to provide a flexible and powerful tool for the analysis and design of control architectures and for the decomposition of large‐scale optimization problems . A powerful generalization of Newman's measure has been proposed in Reference and here it was shown that systems of high modularity are extremum points of a Hamiltonian function.…”

Section: Introductionmentioning

confidence: 98%

Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

AIChE Journal

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We propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off-site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on-site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed-integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

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Graph representation and distributed control of diffusion-convection-reaction system networks

Cited by 6 publications

References 26 publications

Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

Modularity measures: Concepts, computation, and applications to manufacturing systems

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