A superstructure based approach to chemical reactor network synthesis

Achenie, Luke; Biegler, Lorenz T.

doi:10.1016/0098-1354(90)87003-8

Cited by 98 publications

(57 citation statements)

References 12 publications

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“…One alternative includes the use of convexification techniques similar to the ones presented by Westerlund et al 68,69 in search for global solutions. Our surrogate-based MINLP model includes the surrogate models of the permanent units, the reformulation of the surrogate models we just introduced for conditional units, and the algebraic equivalents of the logic constraints in (13).…”

Section: Superstructure Modeling Approachmentioning

confidence: 99%

Surrogate‐based superstructure optimization framework

2011

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in Wiley Online Library (wileyonlinelibrary.com).In principle, optimization-based ''superstructure'' methods for process synthesis can be more powerful than sequential-conceptual methods as they account for all complex interactions between design decisions. However, these methods have not been widely adopted because they lead to mixed-integer nonlinear programs that are hard to solve, especially when realistic unit operation models are used. To address this challenge, we develop a superstructure-based strategy where complex unit models are replaced with surrogate models built from data generated via commercial process simulators. In developing this strategy, we study aspects such as the systematic design of process unit surrogate models, the generation of simulation data, the selection of the surrogate's structure, and the required model fitting. We also present how these models can be reformulated and incorporated into mathematical programming superstructure formulations. Finally, we discuss the application of the proposed strategy to a number of applications.

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Section: Superstructure Modeling Approachmentioning

confidence: 99%

Surrogate‐based superstructure optimization framework

2011

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“…Extensively studied application known as Van de Vusse, for which global solution has not been claimed so far, is chosen to test the ontology-supported optimization (Achenie and Biegler, 1990;Kokossis and Floudas, 1994;Mehta and Kokossis, 1998). First to examine is the impact of the sequence of the 1 st reactor r 1 and 2 nd reactor r 2 on the optimization performance.…”

Section: A Case Study Of Van De Vusse Applicationmentioning

confidence: 99%

“…An extensive use of both deterministic and stochastic optimization algorithms to solve reactor network design synthesis problems have been reported (Achenie and Biegler, 1990;Kokossis and Floudas, 1990;Marcoulaki and Kokossis, 1996;Mehta and Kokossis, 2000;Yang, Linke and Kokossis, 2006). To improve computational performance, recent research effort was directed towards the use of computer grid techniques with Tabu Search algorithm (Du, Yang, Kokossis and Linke, 2007) and Simulated Annealing (SA) and Cascade algorithm (CA) (Yang, 2009;Du, 2009).…”

Section: Introductionmentioning

confidence: 99%

Integration of ontology and knowledge-based optimization in process synthesis applications

Cecelja¹,

Kokossis²,

Du³

2011

Computer Aided Chemical Engineering

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“…Unlike the synthesis of energy or separation systems, this area has seen relatively little development and still contains a number of open, unresolved problems. Moreover, as with many optimization-based approaches to process synthesis, most research relating to reactor networks has concentrated on superstructure optimization (Chitra and Govind, 1985;Pibouleau et al, 1988;Kokossis and Floudas, 1990;Achenie and Biegler, 1990). With this approach, a network is constructed that captures a large family of potential solutions, to be determined by a (mixed integer) optimization strategy.…”

Section: Targetting For Reactor Network Using Optimization Toolsmentioning

confidence: 99%

From nonlinear programming theory to practical optimization algorithms: A process engineering viewpoint

Biegler¹

1993

Computers &Amp; Chemical Engineering

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As process optimization becomes an established and mature technology for process simulation, analysis and operation, it becomes important to consider larger and more creative problem formulations and applications, as well as the development of efficient algorithms to tackle them. Here, some novel process optimization applications are briefly reviewed in the following areas of process design and analysis:• treatment of constrained and nonsmooth simulation problems, particularly in phase equilibrium • synthesis of chemical reactor networks through the application of targeting strategies • more efficient strategies for process optimization and postoptimality analysis • treatment of uncertainty through multiperiod optimization problems • the role of automatic differentiation tools in process optimization An outline of the concepts behind each of these topics will be presented and process examples are briefly described in order to demonstrate the effectiveness of these optimization-based applications.

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A superstructure based approach to chemical reactor network synthesis

Cited by 98 publications

References 12 publications

Surrogate‐based superstructure optimization framework

Surrogate‐based superstructure optimization framework

Integration of ontology and knowledge-based optimization in process synthesis applications

From nonlinear programming theory to practical optimization algorithms: A process engineering viewpoint

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