A framework for process design and control

Mohideen, M.J.; Perkins, John D.; Pistikopoulos, E.N.

doi:10.1049/cp:19960675

Cited by 6 publications

(3 citation statements)

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“…As described, this approach has a few similar features to simultaneous and sequential optimization procedures presented by Mohideen et al (1996a) and the algorithm of Deb and Sinha (2008). In practice, this approach is able to handle dynamic and multiobjective optimization problems on both levels.…”

Section: Solution Proceduresmentioning

confidence: 99%

Bi-level optimization for a dynamic multiobjective problem

Linnala¹,

Madetoja²,

Ruotsalainen³

et al. 2012

Engineering Optimization

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Section: Solution Proceduresmentioning

confidence: 99%

Bi-level optimization for a dynamic multiobjective problem

Linnala¹,

Madetoja²,

Ruotsalainen³

et al. 2012

Engineering Optimization

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“…Luyben and Floudas , proposed a method to consider steady-state economics and open-loop controllability objectives within the mathematical programming framework of process synthesis simultaneously. In the Mohideen et al framework, the process system is modeled using dynamic mathematical models where variations include both uncertain parameters and time-varying disturbances while control structure selection and controller design is considered to be part of the resulting stochastic mixed-integer optimal control mathematical formulation (MIDO). The disadvantage of this approach is that the resulting mixed-integer nonlinear programs (MINLPs) are very large, even for relatively small-scale differential-algebraic equation (DAE) systems, which potentially prohibits the solution of large, realistically modeled systems.…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Design and Analysis of Reaction−Separation Systems with Recycle. 2. Design and Control Integration

Ramírez

Gani

2007

Ind. Eng. Chem. Res.

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The simultaneous solution of the design and control problems of chemical processes represents a complex task, given that several aspects should be fulfilled (e.g., although a design can be considered to be feasible, when attempting to control it might not be possible, or the control configuration could be quite complex). The interaction between design and process variables also must be considered while attempting to outline a control configuration. The second part of this communication shows how, through a model-based methodology, it is possible to solve the design and control problems in a systematic way, leading to a full understanding of a chemical process. The Tennessee-Eastman (TE) problem has been selected to highlight the application of the model-based methodology in a plantwide problem.

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“…Mo ¨nnigmann and Marquardt, 32 on the other hand, incorporated constraints in the system dynamics into the optimization-based design of nonlinear systems. In the Mohideen et al 33 framework, the process system is modeled using dynamic mathematical models where variations include both uncertain parameters and time-varying disturbances and control structure selection, whereas the controller design is considered as part of the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Design and Analysis of Reaction−Separation Systems with Recycle. 1. The Design Perspective

Ramírez

Gani

2007

Ind. Eng. Chem. Res.

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A three-stage systematic model-based analysis methodology for the integrated design-control for chemical process with a reaction-separation with recycle scheme is presented. At the core of the methodology, a model-based analysis of first-principles models of different complexities identifies the important interactions between the design and process variables, so that specified design targets may be achieved. Stage 1 in the model-based methodology simplifies the design-control problem, such that the main operations are identified. The corresponding model, which has been developed in terms of dimensionless variables, helps to identify the limiting values of a set of lumped (design process) variables and locates operational windows where process feasibility can be assessed. In stage 2, more-detailed models are developed by delumping the set of lumped variables and relaxing the assumptions of stage 1. The only solutions to be considered are those lying within the bounds (operation windows) defined in stage 1. The objective of stage 2 is to identify the location of an optimal operation based on a specified design target. Stage 3 is used as a verification stage, by means of more-rigorous models in steady-state and dynamic models. Through the solution of a hierarchical subproblems, the solution of a complex design-control problem is therefore obtained systematically, along with an understanding of the process behavior. The ethylbenzene production process is used to highlight the design features of the methodology.

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A framework for process design and control

Cited by 6 publications

References 0 publications

Bi-level optimization for a dynamic multiobjective problem

Bi-level optimization for a dynamic multiobjective problem

Methodology for the Design and Analysis of Reaction−Separation Systems with Recycle. 2. Design and Control Integration

Methodology for the Design and Analysis of Reaction−Separation Systems with Recycle. 1. The Design Perspective

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