Simultaneous design and control of processes under uncertainty: A robust modelling approach

Sandoval, Luis A. Ricardez; Budman, Hector; Douglas, Peter

doi:10.1016/j.jprocont.2007.11.006

Cited by 68 publications

(23 citation statements)

References 14 publications

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“…These methods are discussed in the following. Douglas and co-workers proposed a method based on model reduction, (Chawanku, et al 2005;Ricardez-Sandoval, et al, 2008. The idea is to perform process identification on the nonlinear first principles model.…”

Section: Methods Based On Model Reduction and Linear Control Theorymentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

116

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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: Methods Based On Model Reduction and Linear Control Theorymentioning

confidence: 99%

Integration of process design and control: A review

Sharifzadeh

2013

Chemical Engineering Research and Design

116

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“…The inversely controlled process model for the case of series reactors consist of Eqns. (3)(4)(5)(6)(7)(8)(9)(10) and the following equations.…”

Section: Inversely Controlled Process Model For the Case Of Two Seriementioning

confidence: 99%

“…Sakizlis, Perkins and Pistikopoulos [4] reviewed the optimization methods applied to co-design, and classified them based on their decision-making criteria which may require steady-state or dynamic analysis. The type of modelling technique (linear or nonlinear, steady state or dynamic) which should be used in the co-design framework and the type of information (controllability, robustness, operability) that can be extracted from such a model is ongoing research, [5][6][7][8]. Sakizlis, et al [4] concluded that future research must focus on application of control theory in the optimization formulation.…”

Section: Introductionmentioning

confidence: 99%

Integrated design and control using a dynamic inversely controlled process model

Sharifzadeh

Thornhill

2013

Computers & Chemical Engineering

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The profitability of chemical processes depends on their design and control. In practice, however, the design and control are often done in sequence, leaving little room for improving the control performance when the process design is already fixed. Many commentators have suggested design and control should be integrated. The difficulty associated with the integrated approach, however, is that the number of alternative designs increases with the size of the process. This article proposes an optimization framework using an inversely controlled process model. The combinatorial complexities associated with the controllers are disentangled from the formulation, but the process and its control structure are still designed simultaneously. This article also considers the effect of uncertainties on the optimal design. The proposed optimization framework is benchmarked on a case study from the literature using stochastic mixed integer dynamic programming. The results demonstrate the advantages of the proposed optimization framework.

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“…In that regard, power series expansions (PSEs) have been used as a basis to capture the behavior of the system for optimal process improvement under uncertainty . Model‐based approaches have also been proposed where the nonlinear behavior of the system is approximated using suitable model structures . Likewise, dynamic high‐fidelity models of the process are represented using approximation and model reduction techniques for simultaneous design and control .…”

Section: Introductionmentioning

confidence: 99%

A trust‐region framework for integration of design and control

Rafiei

Ricardez‐Sandoval

2020

AIChE Journal

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A trust‐region approach is presented to address the simultaneous design and control of large‐scale systems under uncertainty. The key idea is to represent the system using power series expansions (PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE‐based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near‐optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.

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Simultaneous design and control of processes under uncertainty: A robust modelling approach

Cited by 68 publications

References 14 publications

Integration of process design and control: A review

Integration of process design and control: A review

Integrated design and control using a dynamic inversely controlled process model

A trust‐region framework for integration of design and control

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