Nonlinear dynamics and control of process systems with recycle

Kumar, Aditya; Daoutidis, Pródromos

doi:10.1016/s0959-1524(01)00014-2

Cited by 128 publications

(90 citation statements)

References 21 publications

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“…Control of concentration, in this case, is equivalent to the control of reactor temperature and so, we control T R (y 1 ) instead of c A . To derive the controllers, we require ODE description of the model (12). With the slow energy dynamics expressed in terms of T R (rather than the overall network enthalpy) the "algebraic" variables are:…”

Section: Simulation Resultsmentioning

confidence: 99%

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Dynamics and control of reactor - feed effluent heat exchanger networks

Jogwar

Bâldea²,

Daoutidis

2008

2008 American Control Conference

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Abstract-In this paper, we analyze the energy dynamics of process networks comprising of a chemical reactor and a feed effluent heat exchanger (FEHE). Using singular perturbation analysis, we show that, in the case of tight energy integration, the energy dynamics of the network evolves over two time scales, with the enthalpy of individual units evolving in the fast time scale and the overall network enthalpy evolving in the slow time scale. We describe a model reduction procedure to derive the non-stiff slow model which can be used for controller design. The theoretical results are illustrated via a simulation case study.

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Section: Simulation Resultsmentioning

confidence: 99%

“…3 We can note an analogy of this analysis with the case of material recycle [12], wherein for the case with large material recycle, the dynamics of the individual units evolve in the fast time scale and the dynamics of the overall network evolve in a slow time scale.…”

Section: Modeling and Model Reduction Of Fehe-integrated Processesmentioning

confidence: 97%

Dynamics and control of reactor - feed effluent heat exchanger networks

Jogwar

Bâldea²,

Daoutidis

2008

2008 American Control Conference

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“…For example, mass transport and chemical reaction effects may happen at very different rates. Furthermore, interaction effects can induce time-scale separation even when not present in the isolated process units [4] . 4) Process Models often have significant model-plant mismatch, as complex physical-chemical first principle models often need to be simplified for control design.…”

Section: Introductionmentioning

confidence: 99%

Distributed control of chemical process networks

Tippett

Bao

2015

Int. J. Autom. Comput.

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Abstract:In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.

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“…This theory could be applied to various types of networked systems previously examined with other methods, such as plant-wide (integrated) process networks (Luyben et al, 1999), (Hangos et al, 1999), (Gilles, 1998), (Kumar & Daoutidis, 2002), chemical reaction networks (Fishtik et al, 2004), or biological networks (Majewski & Domach, 1989), (Hatzimanikatis et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Complex Process Networks: Passivity and Optimality

Jillson

Ydstie

2005

IFAC Proceedings Volumes

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We introduce a novel modelling framework for studying dynamics, distributed control, and optimization of complex networks made up of chemical processes. We are interested in developing self-organizing structures so that stability and optimality follows as a consequence of how the networks are put together and how they are connected with boundary conditions or other networks. By considering only the topology of the system and basic conservation principles, a result analogous to Tellegen's Theorem of electrical circuit theory is produced. Using this result and passivity theory, a network is shown to converge to a stationary point when the flow relationships are positive. Also, under similar conditions, it is shown that the network is self-optimizing in that the entropy production is minimized.

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Nonlinear dynamics and control of process systems with recycle

Cited by 128 publications

References 21 publications

Dynamics and control of reactor - feed effluent heat exchanger networks

Dynamics and control of reactor - feed effluent heat exchanger networks

Distributed control of chemical process networks

Complex Process Networks: Passivity and Optimality

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