Coordinator MPC for maximizing plant throughput

Aske, Elvira Marie B.; Strand, Stig; Skogestad, Sigurd

doi:10.1016/j.compchemeng.2007.05.012

Cited by 71 publications

(20 citation statements)

References 23 publications

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“…Some applications of optimizing control can be found in refs. . Limited computation power and lack of efficient optimization algorithms have caused that most of the previous works in the field of the optimizing control to focus on lumped‐parameter systems, or slow processes when distributed systems are considered, e.g., chromatographic columns .…”

Section: Introductionmentioning

confidence: 99%

Optimizing Control and State Estimation of a Continuous Polymerization Process in a Tubular Reactor with Multiple Side‐Streams

Hashemi

Kohlmann

Engell

2016

Macro Reaction Engineering

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reactors by continuous ones. The reactor system which is investigated here comprises several inputs and due to the near plug fl ow characteristics between the inputs and the outputs, reacts to the changes of the infl ows with large time delays. Model-based optimizing control is the obvious candidate to control such a multi-input plants with significant delays. While the standard implementations of model predictive control penalize the deviations of the states or outputs from reference values and the control moves in the cost function, in this work the controller is formulated to account directly for the economics of the reactor system while the product quality parameters are imposed as constraints. [ 1 ] Some applications of optimizing control can be found in refs. [ 27 -30 ] . Limited computation power and lack of effi cient optimization algorithms have caused thatIn this contribution, a nonlinear model-based optimizing control for continuous polymerization of acrylic acid in tubular reactors with multiple side injections of monomer is developed. The background of this work is to transfer the production of polymers from semibatch to continuous operations. The confi guration of the tubular reactor, which imposes long delays between the inputs and the measurements, and the lack of intermediate measurements as well as the nonlinear reaction kinetics and sharp moving fronts of concentrations when the infl ows are changed, make the application of the optimizing control very challenging. In order to simulate the sharp fronts of the reactor system faithfully and fast, the spatial domain of the partial differential equations model of the tubular reactor is discretized by applying the weighted essentially non-oscillatory scheme. We formulate the controller such that it optimizes the productivity of the reactor directly, while the product quality parameters are imposed as constraints. A particle fi lter is implemented to estimate the states of the reactor system and to initialize the process model. The simulation results show that the controller can increase the product throughput considerably compared to an initial operating point and has a robust performance against the measurement noise. Furthermore, the effect of formulating the quality constraints as hard or soft constraints as well as a changeover scenario between the different grades of a polymer are studied.

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Section: Introductionmentioning

confidence: 99%

Optimizing Control and State Estimation of a Continuous Polymerization Process in a Tubular Reactor with Multiple Side‐Streams

Hashemi

Kohlmann

Engell

2016

Macro Reaction Engineering

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“…This assumption leads to a poor system performance (Sandell Jr et al, 1978;Šiljak, 1996). Therefore, there is a need for a cross-functional integration between the decentralised controllers, in which a coordination level performs steady-state target calculation for decentralised controller (Aguilera & Marchetti, 1998;Aske et al, 2008;Cheng et al, 2007;Zhu & Henson, 2002). Several distributed MPC formulations are available in the literature.…”

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Control Based on Dynamic Games

Sánchez¹,

Giovanini²,

Murillo³

et al. 2011

Advanced Model Predictive Control

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Distributed Model Predictive Control BasedonDynamicGames 3 different from its prediction. At instant k, the controller solves the optimisation problem min U(k) J (x(k), U(k)) st. (1) X(k + 1)=Γx(k)+HU(k) U(k) ∈U where Γ and H are the observability and Haenkel matrices of the system (Maciejowski, 2002) and the states and input trajectories at time k are given by X(k)=[x(k, k) ••• x(k + V, k)] T V > M, U(k)=[u(k, k) ••• u(k + M, k)] T. The integers V and M denote the prediction and control horizon. The variables x(k + i, k) and u(k + i, k) are the predicted state and input at time k + i based on the information at time k and system model x(k + 1)=Ax(k)+Bu(k),(2) where x(k) ∈ R n x and u(k) ∈U ⊆R n u. The set of global admissible controls U = {u ∈ R n u |Du ≤ d, d > 0} is assumed to be non-empty, compact and convex set containing the origin in its interior.

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“…Rawlings and Mayne [2009], Mayne et al [2000] and Limon et al [2009]. More recently, the interest on economically-oriented NMPC has increased and good practical performance has been reported by many NMPC practitioners ( Zavala and Biegler [2009], , Engell [2007], Aske et al [2008] ). In contrast to the mature body of theoretical basis for tracking-NMPC, there is little stability theory supporting economically-oriented NMPC.…”

Section: Introductionmentioning

confidence: 99%