Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions

Urselmann, Maren; Engell, Sebastian

doi:10.1016/j.compchemeng.2014.08.006

Cited by 29 publications

(19 citation statements)

References 45 publications

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“…To overcome these problems, different approaches have been proposed in the literature. Besides the hybrid method presented in the previous section, some mathematical approaches reduce the complexity of the optimization model by reducing the number of design variables or alternatives, proposing new modeling, applying iterative procedures, or including decomposition techniques as detailed by Urselmann and Engell (2015). However, these approaches exhibit some drawbacks: exclusion of the globally optimal solution for the approach based on the reduction of the complexity and convergence to local optima (for large-scale problems) of the MINLP, which depends on initialization.…”

Section: Optimization At Process Levelmentioning

confidence: 99%

“…To overcome some previous weaknesses and intensify and shorten the process synthesis task, Urselmann and Engell (2015) introduced a Memetic Algorithm (MA). MAs are population-based evolutionary algorithms, which are combined with local optimization strategies.…”

Section: Optimization At Process Levelmentioning

confidence: 99%

“…Furthermore, the design alternative under study is evaluated only if the simulation converges. This methodology has been used to solve the superstructure of a reactive distillation column with an optional external reactor (Urselmann and Engell, 2015). To represent all suitable design alternatives, the superstructure comprises the following:…”

Section: Optimization At Process Levelmentioning

confidence: 99%

“…For other cases, one can consider that if the problem is linear, due to the realized assumptions, one should obtain convergence with a reasonable computation time provided that the problem does not contain too many integer variables. For the method developed by Urselmann and Engell (2015), which is stochastic, one should obtain a result without being certain that the "slave" program converges at each iteration due to initialization problems, which should be different according to the envisaged conditions. The second criterion relates to the assumptions and uncertainties of the level considered.…”

Section: Analyses and Perspectivesmentioning

confidence: 99%

See 3 more Smart Citations

From conceptual design to process design optimization: a review on flowsheet synthesis

Montastruc

Belletante

Pagot

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This paper presents the authors’ perspectives on some of the open questions and opportunities in Process Systems Engineering (PSE) focusing on process synthesis. A general overview of process synthesis is given, and the difference between Conceptual Design (CD) and Process Design (PD) is presented using an original ternary diagram. Then, a bibliometric analysis is performed to place major research team activities in the latter. An analysis of ongoing work is conducted and some perspectives are provided based on the analysis. This analysis includes symbolic knowledge representation concepts and inference techniques, i.e., ontology, that is believed to become useful in the future. Future research challenges that process synthesis will have to face, such as biomass transformation, shale production, response to spaceflight demand, modular plant design, and intermittent production of energy, are also discussed.

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Section: Optimization At Process Levelmentioning

confidence: 99%

Section: Optimization At Process Levelmentioning

confidence: 99%

Section: Optimization At Process Levelmentioning

confidence: 99%

Section: Analyses and Perspectivesmentioning

confidence: 99%

See 2 more Smart Citations

From conceptual design to process design optimization: a review on flowsheet synthesis

Montastruc

Belletante

Pagot

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…For addressing the global optimization with many discrete decision variables, hybrid algorithms combining metaheuristic algorithms and mathematical programming (memetic algorithm) become popular. For example, Urselmann et al [229] proposed a two-level memetic algorithm, where the upper level the integrity constraints and discontinuous cost functions are handled by genetic algorithm, while in the lower level continuous sub-problems are efficiently solved by robust solvers of mathematical programming for state variables [230].…”

Section: Superstructure-based Modeling and Solvingmentioning

confidence: 99%

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

et al. 2018

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To reach optimal/better conceptual designs of energy systems, key design variables should be optimized/adapted with system layouts, which may contribute significantly to system improvement. Layout improvement can be proposed by combining system analysis with engineers’ judgments; however, optimal flowsheet synthesis is not trivial and can be best addressed by mathematical programming. In addition, multiple objectives are always involved for decision makers. Therefore, this paper reviews progressively the methodologies of system evaluation, optimization, and synthesis for the conceptual design of energy systems, and highlights the applications to thermal power plants, which are still supposed to play a significant role in the near future. For system evaluation, both conventional and advanced exergy-based analysis methods, including (advanced) exergoeconomics are deeply discussed and compared methodologically with recent developments. The advanced analysis is highlighted for further revealing the source, avoidability, and interactions among exergy destruction or cost of different components. For optimization and layout synthesis, after a general description of typical optimization problems and the solving methods, the superstructure-based and -free concepts are introduced and intensively compared by emphasizing the automatic generation and identification of structural alternatives. The theoretical basis of the most commonly-used multi-objective techniques and recent developments are given to offer high-quality Pareto front for decision makers, with an emphasis on evolutionary algorithms. Finally, the selected analysis and synthesis methods for layout improvement are compared and future perspectives are concluded with the emphasis on considering additional constraints for real-world designs and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice.

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Memetic Algorithms: A Contemporary Introduction

Cotta

Gallardo

Mathieson

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search processes within a population‐based optimization approach. We discuss this basic structure as well as several of the issues arising in the design process. This paves the way for providing a glimpse of some algorithmic extensions of this basic scheme. After providing an overview of the numerous practical applications of MAs, we close this article with some current perspectives of these techniques.

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Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions

Cited by 29 publications

References 45 publications

From conceptual design to process design optimization: a review on flowsheet synthesis

From conceptual design to process design optimization: a review on flowsheet synthesis

A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants

Memetic Algorithms: A Contemporary Introduction

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