Multitime-scale approach to real-time simulation of stiff dynamic systems

Martínez, Ernesto; Drozdowicz, Bartolomé

doi:10.1016/0098-1354(89)85050-1

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…From a formal point of view this division of Y into Y d and Y s could be carried out by technique of singular perturbations used in modeling and control of multitime-scale systems (Kokotovic et al, 1976;Martinez and Drozdowicz, 1989). However, in many cases (including the FCC), the decomposition is obvious.…”

Section: Partial Control General Principlesmentioning

confidence: 99%

Dynamics and Control of Fluidized Catalytic Crackers. 3. Designing the Control System: Choice of Manipulated and Measured Variables for Partial Control

Arbel

Rinard

Shinnar

1996

Ind. Eng. Chem. Res.

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In this paper is presented an evaluation of different control structures for a fluidized catalytic cracker. A systematic methodology for evaluating such structures was developed which is suitable for complex nonlinear processes in which the number of manipulated variables is smaller than the number of variables that are part of the specifications. It is shown that the choice of the control structure and the variables entering it is far more important than that of the multivariable algorithms to be used. Criteria for the choice of the measured and manipulated variables entering the dynamic structure are presented. The role of additional so-called slow variables in steady state control is discussed and demonstrated by examples. It is shown that linear algorithms are sufficient for dynamic control but nonlinear considerations dominate the choice of the control structure. This paper is one of the first that thoroughly discusses the compromises and choices a designer faces in designing a control system for a complex nonlinear system. Areas in need of further research are outlined.

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Section: Partial Control General Principlesmentioning

confidence: 99%

Dynamics and Control of Fluidized Catalytic Crackers. 3. Designing the Control System: Choice of Manipulated and Measured Variables for Partial Control

Arbel

Rinard

Shinnar

1996

Ind. Eng. Chem. Res.

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“…For instance, if a plant is modeled using a network approach, , then lumping the capacities (i.e., the control volumes) reduces the number of differential equations . If the model has multiple time-scale behavior, then singular perturbation can be used for the reduction of the dynamics of the model. , For one-dimensional distributed systems, the aggregation model reduction method , is another solution to reduce the model’s dynamics. In addition, we quote the balanced truncation method. , This model reduction method reduces the dynamics of the model and maintains the same input−output behavior of the full model.…”

Section: Self-adaptive Nonlinear Model Predictive Controlmentioning

confidence: 99%

“…65 If the model has multiple time-scale behavior, then singular perturbation can be used for the reduction of the dynamics of the model. 66,67 For one-dimensional distributed systems, the aggregation model reduction method 68,69 is another solution to reduce the model's dynamics. In addition, we quote the balanced truncation method.…”

Section: The Model Boundsmentioning

confidence: 99%

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Dones

Manenti

Preisig

et al. 2010

Ind. Eng. Chem. Res.

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Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future. Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter.

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Diagnostic Goal-Driven Reduction of Multiscale Process Models

Németh

Lakner

Hangos

Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena

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A systematic model reduction method is proposed in this paper that is applicable for multiscale process models with steady-state operating points for prediction-based fault detection, isolation and loss prevention in the initial phase of a fault. Based on the time-scale separation of the dynamics a refined scale-map, a model structure map is constructed for each root cause and symptom and the relevant target time scale is determined. Then the scale-reduced nonlinear model of the system is constructed that only contains the dynamic balance equations on the target time scale with all the other dynamic equations reduced by steady-state assumptions. Thereafter local linearization is applied to obtain the final reduced model.The resulted set of reduced models preserves the engineering meaning of the remaining state variables, while the number of state variables and parameters is decreased significantly.

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Multitime-scale approach to real-time simulation of stiff dynamic systems

Cited by 7 publications

References 8 publications

Dynamics and Control of Fluidized Catalytic Crackers. 3. Designing the Control System: Choice of Manipulated and Measured Variables for Partial Control

Dynamics and Control of Fluidized Catalytic Crackers. 3. Designing the Control System: Choice of Manipulated and Measured Variables for Partial Control

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Diagnostic Goal-Driven Reduction of Multiscale Process Models

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