A Memetic Algorithm for Global Optimization in Chemical Process Synthesis Problems

Urselmann, Maren; Barkmann, Sabine; Sand, Guido; Engell, Sebastian

doi:10.1109/tevc.2011.2150753

Cited by 37 publications

(25 citation statements)

References 47 publications

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“…Moreover, stochastic optimisation methods are easy to implement and do not require the calculation of derivatives of the objective function, making the optimisation procedure very robust, which is especially advantageous for the optimisation of largescale, nonlinear, nonconvex problems. The two most important examples of stochastic algorithms that were used to optimise RD processes are the simulated annealing method (Cardoso et al, 2000;Gomez et al, 2006;Kiss et al, 2012) and evolutionary algorithms (Babu and Khan, 2007;Rahman et al, 2008;Urselmann et al, 2011). An evolutionary algorithm was applied in the present work to optimise the RD column described in Section 2.2 towards EMC or DEC selectivity while still having high reactant conversions.…”

Section: Optimisationmentioning

confidence: 99%

Reactive Distillation for Multiple-Reaction Systems: Optimisation Study Using an Evolutionary Algorithm

Keller¹,

Dreisewerd²,

Górak³

2013

Chemical and Process Engineering

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Reactive distillation (RD) has already demonstrated its potential to significantly increase reactant conversion and the purity of the target product. Our work focuses on the application of RD to reaction systems that feature more than one main reaction. In such multiple-reaction systems, the application of RD would enhance not only the reactant conversion but also the selectivity of the target product. The potential of RD to improve the product selectivity of multiple-reaction systems has not yet been fully exploited because of a shortage of available comprehensive experimental and theoretical studies. In the present article, we want to theoretically identify the full potential of RD technology in multiple-reaction systems by performing a detailed optimisation study. An evolutionary algorithm was applied and the obtained results were compared with those of a conventional stirred tank reactor to quantify the potential of RD to improve the target product selectivity of multiple-reaction systems. The consecutive transesterification of dimethyl carbonate with ethanol to form ethyl methyl carbonate and diethyl carbonate was used as a case study.

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Section: Optimisationmentioning

confidence: 99%

Reactive Distillation for Multiple-Reaction Systems: Optimisation Study Using an Evolutionary Algorithm

Keller¹,

Dreisewerd²,

Górak³

2013

Chemical and Process Engineering

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“…Moscato (1989) first introduced a memetic algorithm that combines population-based stochastic algorithms with local refinement strategies. Since then, many combined stochastic and deterministic algorithms have been proposed and applied to various optimization problems including chemical process synthesis (Athier et al, 1997;Urselmann et al, 2011a;Skiborowski et al, 2015) and industry-scale distillation and reactive distillation design (Gómez et al, 2006;Urselmann et al, 2011b). Studies have shown that the optimization approach that combines advantages of stochastic and deterministic algorithms can considerably improve the optimization performance in terms of solution quality and computational cost (Lima et al, 2006;Molina et al, 2010).…”

Section: Hybrid Stochastic-deterministic Algorithmmentioning

confidence: 99%

A hybrid stochastic–deterministic optimization approach for integrated solvent and process design

Zhou

Sundmacher

2017

Chemical Engineering Science

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The best solution to computer-aided solvent and process design problems can be only achieved by the simultaneous optimization of solvent molecules and process operating conditions. In this contribution, a hybrid stochastic-deterministic optimization approach is proposed for integrated solvent and process design. It is a combination of a genetic algorithm (GA) that optimizes the discrete molecular variables and a gradient-based deterministic algorithm that solves the continuous nonlinear optimization problem of the process at fixed molecular variables as proposed by the GA. The method is demonstrated on a coupled absorption-desorption process where solvent molecular structures as well as the operating conditions of the absorption and desorption columns are optimized simultaneously. While deterministic mixed-integer nonlinear programming (MINLP) algorithms rely on well-selected initial estimates, the proposed hybrid approach can reliably and steadily solve the problem under random initializations. The combination of the advantages of stochastic and deterministic algorithms makes the approach a promising alternative to conventional MINLP algorithms for solving integrated solvent and process design problems.

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“…It has been shown by Urselmann et al (2011) and Tometzki and Engell (2010) that hybrid evolutionary algorithms (also called memetic algorithms) are well suited for solving large scale planning and design problems. As the problem size in process synthesis can be quite large, and typical flowsheet synthesis problems have a significant combinatorial aspect, a hybrid evolutionary algorithm was chosen.…”

Section: The Optimization Algorithmmentioning

confidence: 99%

A framework for the modeling and optimization of process superstructures under uncertainty

Steimel

Harrmann

Schembecker

et al. 2014

Chemical Engineering Science

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A Memetic Algorithm for Global Optimization in Chemical Process Synthesis Problems

Cited by 37 publications

References 47 publications

Reactive Distillation for Multiple-Reaction Systems: Optimisation Study Using an Evolutionary Algorithm

Reactive Distillation for Multiple-Reaction Systems: Optimisation Study Using an Evolutionary Algorithm

A hybrid stochastic–deterministic optimization approach for integrated solvent and process design

A framework for the modeling and optimization of process superstructures under uncertainty

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