Feedforward output-feedback control for continuous exothermic reactors with isotonic kinetics

Díaz-Salgado, Jorge; Álvarez, Jesús; Schaum, Alexander; Moreno, Jaime A.

doi:10.1016/j.jprocont.2011.07.005

Cited by 15 publications

(13 citation statements)

References 21 publications

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“…It must be pointed out that an estimator with the "calorimetric" counterpart of the S-observer (40) structure (39) has been employed to set an application-oriented software sensor-based PI concentration controller for an exothermic chemical reactor with temperature measurement, PI temperature cascade control, and unmeasured time-varying feed concentration [28]. The software sensor asymptotically estimates the reaction rate and the concentration, and the measurement injection is performed by means of reduced-order observer with linear innovation.…”

Section: Isotonic Kineticsmentioning

confidence: 99%

On the estimation problem of a class of continuous bioreactors with unknown input

Moreno

Álvarez

2015

Journal of Process Control

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Section: Isotonic Kineticsmentioning

confidence: 99%

On the estimation problem of a class of continuous bioreactors with unknown input

Moreno

Álvarez

2015

Journal of Process Control

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“…is defined after (8), and is an input that: (i) is observable because it is time-wise uniquely determined ( =̇+ ) by the input-output pair ( , ) (Gonzalez and Alvarez, 2005;Diaz-Salgado et al, 2012), and (ii) the satisfies the matching condition (19b), implying inherent robustness for control design. The elimination in eq.…”

Section: Secondary Controller Redesignmentioning

confidence: 99%

“…( , ) = ( , ) = 1 (19a,b) with unknown time-varying input ( ) that can be on-line estimated arbitrarily fast (up to measurement error, with adjustable exponential convergence rate ) with the reducedorder observer (Gonzalez and Alvarez, 2005;Diaz-Salgado et al, 2012;Porru et al, 2014) ̇= − − ( −̂), (0) = 0, ̂= + (20) The enforcement of the prescribed closed-loop dynamics (7a) of the detailed model-based secondary nonlinear SF temperature controller (8) upon the simplified model (19) followed by the replacement of the input by its observerbased estimate (20) yields the temperature tracking controller: According to the theoretical developments, controller (21) (with one linear ODE and reduced model dependency) yields the same behavior that its detailed model-based counterpart (16c-e) (with 344 nonlinear ODEs).…”

Section: Secondary Controller Redesignmentioning

confidence: 99%

Energy Saving through Control in an Industrial Multicomponent Distillation Column

Porru¹,

Baratti²,

Álvarez³

2015

IFAC-PapersOnLine

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The problem of reducing the energy consumption in an industrial multicomponent distillation column is addressed. The column is subjected to step feed and distillate flow rates, the distillate impurity is regulated by adjusting the heat duty with a PI temperature controller, and the objective is to save energy through control upgrade. First, the nonlinear feedforward output-feedback robust advanced control problem is addressed, drawing the control construction and the solvability conditions. Then, the behavior of the advanced controller is recovered using a PI temperature controller with a dynamic feedforward setpoint compensator driven by the three measured flow rate disturbances. The approach is illustrated and tested through numerical simulations with a model calibrated with industrial data, finding that: the distillate impurity mean is maintained, its variability is reduced by 80 %, and the energy consumption is reduced by 15 %

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“…The analysis of the general case of nonlinear chemical reactions as sources, however, is a challenging task that requires individual analysis. This is seen in [30] where a feedforward output-feedback controller is designed for a simple CSTR with exothermic isotonic kinetics. Therefore, we restrict our study to a simple case here.…”

Section: Stabilizing Linear Sourcesmentioning

confidence: 99%

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

Hangos

Szederkényi

2014

Journal of Process Control

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Based on the structure of process models a hierarchically structured statespace model has been proposed for process networks with controlled mass convection and constant physico-chemical properties. Using the theory of cascade-connected nonlinear systems and the properties of Metzler and Hurwitz matrices it is shown that process systems with controlled mass convection and without sources or with stabilizing linear source terms are globally asymptotically stable. The hierarchically structured model gives rise to a distributed controller structure that is in agreement with the traditional hierarchical process control system structure where local controllers are used for mass inventory control and coordinating controllers are used for optimizing the system dynamics. The proposed distributed controller is illustrated on a simple non-isotherm jacketed chemical reactor.

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Feedforward output-feedback control for continuous exothermic reactors with isotonic kinetics

Cited by 15 publications

References 21 publications

On the estimation problem of a class of continuous bioreactors with unknown input

On the estimation problem of a class of continuous bioreactors with unknown input

Energy Saving through Control in an Industrial Multicomponent Distillation Column

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

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