Simulated annealing for the optimization of batch distillation processes

Hanke, Michael; Li, Pu

doi:10.1016/s0098-1354(00)00317-3

Cited by 64 publications

(31 citation statements)

References 16 publications

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“…But stochastic methods of optimisation can also be used, among which Genetic Algorithm based methods (Fraga et al, 1996, Mukherjee et al, 2001 and simulated annealing methods (Hanke and Li, 2000) can be quoted. The choice of a deterministic or a stochastic method depends on their characteristics.…”

Section: Solving Methods Of Batch Distillation Optimisation Problemsmentioning

confidence: 99%

“…To transform it into a finite size problem, discretisation techniques with respect to time can be used. The time horizon is then cut out in a finite number of intervals: 2 (Sorensen and Skogestad, 1996), 4 (Noda et al, 2001), 5 (Fernholz et al, 1997), 6 (Mujtaba and Macchieto, 1996, 7 (Furlonge et al, 1997) or 40 (Hanke and Li, 2000). This choice results from a compromise between precision from the discretisation and computing time.…”

Section: Batch Distillation Optimisation Variablesmentioning

confidence: 99%

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Heterogeneous batch distillation processes: Real system optimisation

Pommier¹,

Massebeuf²,

Gerbaud³

et al. 2005

Computer Aided Chemical Engineering

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In this paper, optimisation of batch distillation processes is considered. It deals with real systems with rigorous simulation of the processes through the resolution full MESH differential algebraic equations.Specific software architecture is developed, based on the BatchColumn® simulator and on both SQP and GA numerical algorithms, and is able to optimise sequential batch columns as long as the column transitions are set.The efficiency of the proposed optimisation tool is illustrated by two case studies. The first one concerns heterogeneous batch solvent recovery in a single distillation column and shows that significant economical gains are obtained along with improved process conditions. Case two concerns the optimisation of two sequential homogeneous batch distillation columns and demonstrates the capacity to optimize several sequential dynamic different processes. For such multiobjective complex problems, GA is preferred to SQP that is able to improve specific GA solutions.

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Section: Solving Methods Of Batch Distillation Optimisation Problemsmentioning

confidence: 99%

Section: Batch Distillation Optimisation Variablesmentioning

confidence: 99%

Heterogeneous batch distillation processes: Real system optimisation

Pommier¹,

Massebeuf²,

Gerbaud³

et al. 2005

Computer Aided Chemical Engineering

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show abstract

“…They have the ability to use the values from the model in a black box optimization approach with out requiring the derivatives. Various studies have been reported to demonstrate the ability of these methods in providing efficient optimization solutions (Hanke and Li, 2000 ;Shopova and Vaklieva-Bancheva, 2006).…”

Section: Model Predictive Control 128mentioning

confidence: 99%

Model Predictive Control of Nonlinear Processes

Venkateswarlu¹

2010

Model Predictive Control

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“…Research in the literature also evaluates various distillation systems, including batch columns, solar driven membrane systems, and multi-effect columns. From an optimization point of view, most of these use meta-heuristic methods, such as simulated annealing and genetic algorithms, based on the low risk of local optima problems, ease of development, and/or lack of commercial availabilities 9,[11][12][13][14] . In some studies, simulation programs like Aspen and MATLAB are used to evaluate the flowcharts and physical data, in order to decrease the code development time and costs 15 .…”

Section: Introductionmentioning

confidence: 99%

Exergoeconomic Distillation Sequencing by Multi-objective Optimization through a Hybrid Genetic Algorithm

Özçelik

Mert

2016

Chem.Biochem.Eng.Q.

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While trying to optimize sharp distillation processes, the number of possible column sequences significantly increases as the number of components that make up the feed mixture increases. As a result, proper sequencing with maximum exergetic profit and minimum exergy destruction becomes harder to achieve. In this study, an exergoeconomic multi-objective optimization was applied to the distillation sequences of three separate hydrocarbon mixture cases, by means of a genetic-algorithm-based solver software. A computer program (DISMO) was developed in-house to achieve this functionality. The results indicate that the created algorithm was quite applicable in determining the optimum sequencing in distillation, as it successfully created the Pareto Solution Set and suggested the optimum configurations. This study also presented an opportunity to conduct a parametric investigation on various weighting factors for objective functions. As the importance given to a specific objective was increased, the optimization results had a tendency to favour that specific objective through arrangement of sequencing as expected, though the profit and sequencing converged to a single result after a certain threshold.

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Simulated annealing for the optimization of batch distillation processes

Cited by 64 publications

References 16 publications

Heterogeneous batch distillation processes: Real system optimisation

Heterogeneous batch distillation processes: Real system optimisation

Model Predictive Control of Nonlinear Processes

Exergoeconomic Distillation Sequencing by Multi-objective Optimization through a Hybrid Genetic Algorithm

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