A stable elemental decomposition for dynamic process optimization

Cervantes, Arturo; Biegler, Lorenz T.

doi:10.1016/s0377-0427(00)00302-2

Cited by 31 publications

(19 citation statements)

References 13 publications

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“…It is notable that new OA algorithms (e.g., applied by the recent versions of DICOPT) are extened to overcome this deficiency. The method of full discretization based on orthogonal collocation was also applied by Cervantes and Biegler (2000b;Biegler (2005;. Flores-Tlacuahuac and Biegler (2007) also studied the effects of the convexities of the problem formulation on the results.…”

Section: Simultaneous Optimization Of a Process And Its Controllersmentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

115

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There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities. KeywordsIntegrated design and control, Sequential design and control, Control structure selection, Simultaneous optimization of a process and its controllers, inversely controlled process model, controllability.

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Section: Simultaneous Optimization Of a Process And Its Controllersmentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

115

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“…where f is an objective function, h is a system of the differential and algebraic equation constraints, which can also be divided into two separate systems Grossmann 2004, Cervantes andBiegler 2000), x x x denotes the differential and algebraic state vectors (these can also be presented separately), u u u is the vector of optimization variables, also known as a control vector, p p p is a steady parameter vector, and t f is a length of time horizon. Sometimes the steady parameter vectors can be ignored, because they are constants from the optimization point of view.…”

Section: Dynamic Optimizationmentioning

confidence: 99%

Bi-level optimization for a dynamic multiobjective problem

Linnala¹,

Madetoja²,

Ruotsalainen³

et al. 2012

Engineering Optimization

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“…The decision variables in the resulting optimization problem are these unspecified parameters. Parameterization falls into two categories: (a) complete parameterization (CP), also known as the "simultaneous strategy," which discretizes both state and control variables [2], [3]; (b) control vector parameterization (CVP), also referred to as the "sequential strategy," which only discretizes control variables [4]- [11]. Biegler et al conduct a great deal of work on simultaneous strategy for dynamic process optimization, see a review on the state-of-the-art in CP methods [3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Industrial Processes With Nonuniform Discretization-Based Control Vector Parameterization

Tianfield

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.Note to Practitioners-This paper was motivated by the problems of operational optimization in industrial processes. Most problems in industrial processes are dynamic optimization problems, and the aim is to optimize control variables like feeding rates, heating/cooling for reaction over a time span. Most of existing optimization methods use uniform discretization-based control vector parameterization (udCVP for short), in which the time span for optimization is partitioned into a multitude of even intervals and thus merely the control parameters need to be optimized. In this paper, we propose a novel scheme of nonuniform-based discretization control vector parameterization (ndCVP), in which the time span is unevenly partitioned, and the incremental time parameters are encoded into the individual to be optimized as well. It is proved that ndCVP is a natural generalization of udCVP. Moreover, by integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new Manuscript optimization method, named ndCVP-HGPSO for short, is formed for DOPs. Numeric simulations show that ndCVP-HGPSO is a highly competitive method for DOPs, because ndCVP can adjust the time intervals to generate high precision results in compliance with the shape of control trajectories.Index Terms-Dynamic optimization, hybrid gradient particle swarm optimization, nonuniform discretizetion-based control vector parameterization.

show abstract

A stable elemental decomposition for dynamic process optimization

Cited by 31 publications

References 13 publications

Integration of process design and control: A review

Integration of process design and control: A review

Bi-level optimization for a dynamic multiobjective problem

Dynamic Optimization of Industrial Processes With Nonuniform Discretization-Based Control Vector Parameterization

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