Nonlinear Model Predictive Control of Chemical Processes

Silva, R. G.; Kwong, Wu Hong

doi:10.1590/s0104-66321999000100008

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…In the simultaneous solution [ER90,PE93,SK99] the differential equations are transformed to algebraic equations which are solved in addition to the nonlinear equality constraints in the optimization. The decision variables includes both the model states and control signals and the model equations are appended to the optimization problem as equality constraints.…”

Section: Simultaneous Methodsmentioning

confidence: 99%

Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

2018

atp

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Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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Section: Simultaneous Methodsmentioning

confidence: 99%

Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

2018

atp

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“…(Kawathekar 2004) The most frequently cited MPC techniques are dynamic matrix control (DMC) and model algorithmic control (MAC) (Silva & Kwong 1999), which are used successfully in a larger number of industrial processes because they explicitly handle constraints (Prett 1979;Moro 1995;Odloak 1996). The objective function is defined in terms of both present and predicted system variables and is evaluated using an explicit model to predict future process outputs.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…Most Silva & Kwong 1999;Kumar & Daoutidis 1999) . Roat et al (1986) presented the inadequacies of conventional linear multi-loop controllers and highlighted the need for more advanced controllers designed within the framework of nonlinear control science.…”

Section: Introductionmentioning

confidence: 99%

Control of Reactive Distillation Column: A Review

Sharma¹,

Singh²

2010

International Journal of Chemical Reactor Engineering

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The objective of this paper is to give a critical survey of the present status within the field of control of a reactive distillation column. Control of a reactive distillation column is a challenging task due to process nonlinearity and complex interactions between the vapor-liquid equilibrium and chemical reactions. There are different types of control methodologies, which have been studied in the reactive distillation, ranging from a simple proportional-integral (PI) controller to advanced model predictive controllers (MPC) such as dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC), robust multivariable predictive control technology (RMPCT), generalized predictive control (GPC), and other advanced control techniques. With the goals of optimal performance, energy conservation and cost effectiveness of process operations in industries, the design of optimal controllers and controller performance assessment have received great attention in both industries and academia. The main objective of control is to maintain the product purity within the desired range.

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“…In our model the first process in each time step is the irreversible conversion of chemical A into product B. The conversion rate is determined by φ k(X 2 ) as in [6], where X 2 is the average temperature of the tank temperature arrangement. This first order kinetics "constant" is multiplied by the time step in order to get the proportion of the reactive A that is expected to be converted into product B in each evolution step.…”

Section: Stochastic Ca Model For Jacketed Cstrmentioning

confidence: 99%

Cellular automata modeling of continuous stirred tank reactors

Pérez-Terrazas

Ibarra-Junquera

Rosu

2008

Korean J. Chem. Eng.

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The classical dynamical systems model of continuous stirred tank reactors (CSTR) in which a first order chemical reaction takes place is reformulated in terms of stochastic cellular automata by extending previous works of Seyborg [3] and Neuforth [4] by including the feed flow of chemical reactants. We show that this cellular automata procedure is able to simulate the dilution rate and the mixing process in the CSTR, as well as the details of the heat removal due to the jacket. The cellular automata approach is expected to be of considerable applicability at any industrial scales and especially for any type of microchemical systems.

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Nonlinear Model Predictive Control of Chemical Processes

Cited by 12 publications

References 17 publications

Echtzeit-Implementierung von NMPC

Echtzeit-Implementierung von NMPC

Control of Reactive Distillation Column: A Review

Cellular automata modeling of continuous stirred tank reactors

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