On the development of novel chemicals using a systematic synthesis approach. Part I. Optimisation framework

Marcoulaki, Eftychia C.; Kokossis, Antonis

doi:10.1016/s0009-2509(99)00522-9

Cited by 74 publications

(73 citation statements)

References 66 publications

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“…branch and bound, 39 branch and reduce 40 ) and stochastic optimization methods (e.g. simulated annealing, 41 genetic algorithms (GAs), 42 and Tabu search 18 ). Approaches that combine features from both domains, such as decomposition methods, have also been previously developed.…”

Section: Solution Of the Underlying Minlpmentioning

confidence: 99%

Computer‐aided design of tailor‐made ionic liquids

Karunanithi

Mehrkesh

2013

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com) Ionic liquids (IL), with their negligible vapor pressure, have the potential to replace volatile organic solvents in several processes. They also exhibit other unique characteristics, such as high thermal stability, wide liquid range, and wide electrochemical window, which make them attractive for many important applications. In addition, millions of ILs can be formed through different combination of cations, anions, and other functional groups. Till now, majority of work on IL selection, for a given application, is guided by trial and error experimentation. In this article, we present a computer-aided IL design framework, based on semiempirical structure-property models and optimization methods, which can consider several IL candidates and design optimal structures for a given application. This powerful methodology has great potential to act as a knowledge-based framework to aid synthetic chemists and engineers develop new ILs.

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Section: Solution Of the Underlying Minlpmentioning

confidence: 99%

Computer‐aided design of tailor‐made ionic liquids

Karunanithi

Mehrkesh

2013

AIChE Journal

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“…Additional criteria such as pressure limits, coefficient of performance and safety are also considered for the selection of the final fluids from the database. CAMD-based approaches addressing the design of refrigerant fluids and/or systems (which also have some similarities with ORC) have also been proposed by Samudra and Sahinidis [47], Sahinidis et al [48], Duvedi and Achenie [49] using deterministic MINLP-based formulations and Marcoulaki and Kokossis [50] using Simulated Annealing. (Hexafluoropropane-R236fa)…”

Section: Optimization-based Camd Of Pure Fluidsmentioning

confidence: 99%

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

2015

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Efficient power generation from low to medium grade heat is an important challenge to be addressed to ensure a sustainable energy future. Organic Rankine Cycles (ORCs) constitute an important enabling technology and their research and development has emerged as a very active research field over the past decade. Particular focus areas include working fluid selection and cycle design to achieve efficient heat to power conversions for diverse hot fluid streams associated with geothermal, solar or waste heat sources. Recently, a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs. This paper presents a review of emerging approaches with a particular emphasis on computer-aided design methods.

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“…Among the conventional optimization techniques, genetic algorithm (GA), simulated annealing (SA), and tabu search (TS) have their advantages. GA is a simulation of natural evolution and creates new better individuals by crossover, mutation, and selection; in this way, GA handles the linear and bound constraints and obtains fitter generations towards the optimazation objective.…”

Section: Introductionmentioning

confidence: 99%

“…Ourique first proposed the use of SA in CAMD. Kokossis et al employed SA to predict the molecular structure, successfully avoiding a combinatorial explosion. Wang and Achenie presented a hybrid global optimization approach that combines the outer approximation (OA) and SA algorithms for several solvent design problems.…”

Section: Introductionmentioning

confidence: 99%

Development of CAMD based on the hybrid gene algorithm and simulated annealing algorithm and the application on solvent selection

Liu

Wen

Zhang³

2016

Can J Chem Eng

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In this paper, an evolutionary approach, improved CAMD based on the hybrid gene algorithm and simulated annealing algorithm (GASA), is developed. The new approach combines the feature of GA and SA, avoiding the problem of prematurity. With a new category strategy of candidate groups from the Mod UNIFAC group database adopted, a repair operator is introduced to guarantee the integrity of randomly generated molecules and thus the search of straight chain alkane as well as cyclane solutions can be performed together. The properties of molecules are obtained by the group contribution method. An example of extractive solvent selection for a methanol‐methyl acetate system has been illustrated in detail to further explain the method. The stability as well as other properties of the molecules which the authors think important is considered in the fitness function. Results of the example show that the fitness ranking values are better than those in the literature. The CAMD method in this paper can be used in practical chemical processes.

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On the development of novel chemicals using a systematic synthesis approach. Part I. Optimisation framework

Cited by 74 publications

References 66 publications

Computer‐aided design of tailor‐made ionic liquids

Computer‐aided design of tailor‐made ionic liquids

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Development of CAMD based on the hybrid gene algorithm and simulated annealing algorithm and the application on solvent selection

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