Quality control of binary distillation columns via nonlinear aggregated models

Lévine, Jean; Rouchon, Pierre

doi:10.1016/0005-1098(91)90104-a

Cited by 105 publications

(68 citation statements)

References 22 publications

(34 reference statements)

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“…In this study, the aggregated modeling method of [Lévine, J., & Rouchon, P. (1991). Quality control of binary distillation columns via nonlinear aggregated models.…”

Section: Introductionmentioning

confidence: 99%

“…For nonlinear model reduction of distillation columns, several model reduction and simplification methods have been developed in the past (Benallou, Seborg, & Mellichamp, 1986;Cho & Joseph, 1983;Khowinij, Bian, Henson, Belanger, & Megan, 2004;Kienle, 2000;Kumar & Daoutidis, 2003;Lévine & Rouchon, 1991;Marquardt, 1990;Skogestad, 1997). Among these are the method of compartmental modeling (Benallou et al, 1986) and later the improved variant of aggregated modeling by Lévine and Rouchon (1991).…”

Section: Introductionmentioning

confidence: 99%

“…Among these are the method of compartmental modeling (Benallou et al, 1986) and later the improved variant of aggregated modeling by Lévine and Rouchon (1991). The latter method is used for deriving the reduced models investigated in this study.…”

Section: Introductionmentioning

confidence: 99%

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Computational performance of aggregated distillation models

Linhart

Skogestad

2009

Computers & Chemical Engineering

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a b s t r a c tCompartmental and aggregated modeling is used to derive low-order (reduced) dynamic models from detailed models of staged processes. In this study, the aggregated modeling method of [Lévine, J., & Rouchon, P. (1991). Quality control of binary distillation columns via nonlinear aggregated models. Automatica, 27, 463] is revised with the objective of deriving computationally efficient models for real-time control and optimization applications. A simple implementation of the original method not requiring the specification of compartments is presented. The resulting DAE models are converted into ODE models by pre-solving and substituting the algebraic equations resulting from the reduction procedure, which is the key step to increase simulation speed. To study this, the performances of several full and reduced distillation models, with and without base-layer controllers, are compared using different numerical integrators. It is found that while the reduced DAE models are computationally not advantageous, the reduced ODE models decrease the simulation time by a factor of 5-10.

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“…In this study, the aggregated modeling method of [Lévine, J., & Rouchon, P. (1991). Quality control of binary distillation columns via nonlinear aggregated models.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational performance of aggregated distillation models

Linhart

Skogestad

2009

Computers & Chemical Engineering

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“…The challenge to reduce the computational cost of such systems motivated the development of different model order reduction techniques, such as compartmental models (España and Landau, 1978;Benallou et al, 1986;Musch and Steiner, 1993) and its variants aggregated modeling (Lévine and Rouchon, 1991;Skogestad, 2009, 2010;Kamath et al, 2011) and time-scale separation (Kumar and Daoutidis, 2003) based on singular perturbation analysis, nonlinear wave propagation (Marquardt, 1986;Kienle, 2000), model linearization (Georgakis and Stoever, 1982), and orthogonal collocation.…”

Section: Introductionmentioning

confidence: 99%

Highly-Accurate Model Order Reduction Technique on a Discrete Domain

Ribeiro

Secchi

Biscaia

2015

Braz. J. Chem. Eng.

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-In this work, we present a highly-accurate technique of model order reduction applied to staged processes. The proposed method reduces the dimension of the original system based on null values of moment-weighted sums of heat and mass balance residuals on real stages. To compute these sums of weighted residuals, a discrete form of Gauss-Lobatto quadrature was developed, allowing a high degree of accuracy in these calculations. The locations where the residuals are cancelled vary with time and operating conditions, characterizing a desirable adaptive nature of this technique. Balances related to upstream and downstream devices (such as condenser, reboiler, and feed tray of a distillation column) are considered as boundary conditions of the corresponding difference-differential equations system. The chosen number of moments is the dimension of the reduced model being much lower than the dimension of the complete model and does not depend on the size of the original model. Scaling of the discrete independent variable related with the stages was crucial for the computational implementation of the proposed method, avoiding accumulation of round-off errors present even in low-degree polynomial approximations in the original discrete variable. Dynamical simulations of distillation columns were carried out to check the performance of the proposed model order reduction technique. The obtained results show the superiority of the proposed procedure in comparison with the orthogonal collocation method.

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“…For high purity distillation column, the two-point control or other strategies have been investigated [1,2]. Considering the intrinsic nonlinearity of a distillation column, nonlinear controller has been designed based on the rigorous mathematical model [3,4]. Although a nonlinear controller may be effective in simulation, its implementation in practical plants is complex because of the lack of measurement of some key variables.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Neural Network Based Nonlinear Control of a Distillation Column

2011

ICA

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Taking advantage of the knowledge of top and bottom compositions of a distillation column, a dynamic neural network (DNN) is designed to identify the input-output relationship of the column. The weight-training algorithm is derived from a Lyapunov function. Based on this empirical model, a nonlinear H  controller is synthesized. The effectiveness of the control strategy is demonstrated using simulation results.

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Quality control of binary distillation columns via nonlinear aggregated models

Abstract: A robust nonlinear control law is designed to reject unknown feed composition disturbances with overall stability. Implementation to real columns as well as comparisons with classical control strategies, show the robustness and flexibility improvements of the method.

Cited by 105 publications

References 22 publications

Computational performance of aggregated distillation models

Computational performance of aggregated distillation models

Highly-Accurate Model Order Reduction Technique on a Discrete Domain

Dynamic Neural Network Based Nonlinear Control of a Distillation Column

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