Pareto front of ideal Petlyuk sequences using a multiobjective genetic algorithm with constraints

Gutiérrez‐Antonio, Claudia; Briones-Ramírez, Abel

doi:10.1016/j.compchemeng.2008.11.004

Cited by 80 publications

(43 citation statements)

References 23 publications

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“…In order to solve this problem, we used the multiobjective genetic algorithm with constraints coupled to Aspen Plus TM proposed by Gutiérrez-Antonio et al [5] to manage five and four objectives along with the 4 constraints of purities, depending on the sequence. The multiobjective genetic algorithm used allows obtaining the rigorous Pareto front of the intensified distillation systems: a set of non dominated, optimal and rigorous designs that satisfied the purities required.…”

Section: Design Tool: Genetic Algorithmmentioning

confidence: 99%

Design and Control of New Intensified Distillation Systems for Quaternary Separations using Genetic Algorithms

Castillo¹,

Sánchez²,

Hernández³

et al. 2009

Computer Aided Chemical Engineering

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The design and optimization of a coupled distillation system is a non-linear and multivariable problem. The complexity of this kind of problem leads to the high difficulty for solving it. This paper addresses the application of genetic algorithms to the optimization of intensified distillation systems for quaternary distillations. For that purpose, we used a multi-objective genetic algorithm with restrictions, written in Matlab TM coupled to process simulator Aspen Plus TM for the evaluation of the objective function.

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Section: Design Tool: Genetic Algorithmmentioning

confidence: 99%

Design and Control of New Intensified Distillation Systems for Quaternary Separations using Genetic Algorithms

Castillo¹,

Sánchez²,

Hernández³

et al. 2009

Computer Aided Chemical Engineering

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“…Derivative-free optimization (DFO) is a class of algorithms designed to solve optimization problems when derivatives are unavailable, unreliable or prohibitively expensive to evaluate. Although there is a vast literature on metaheuristic optimization, combination with chemical process simulators is a relatively recent development (Dantus & High, 1999;Eslick & Miller, 2011;Gutiérrez-Antonio & Briones-Ramírez, 2009;Gutiérrez-Antonio et al, 2011;Leboreiro & Acevedo, 2004;Torres et al, 2013). Although DFO algorithms can be used in models with costly and/or noisy function evaluations, these methods are often constrained to models in which the number of degrees of freedom does not exceed about 10 (Rios & Sahinidis, 2013).…”

Section: Methodsmentioning

confidence: 99%

Large scale optimization of a sour water stripping plant using surrogate models

Quirante

2016

Computers & Chemical Engineering

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Please cite this article as: Quirante, Natalia., & Caballeroa, José A., Large scale optimization of a sour water stripping plant using surrogate models.Computers and Chemical Engineering http://dx.doi.org/10. 1016/j.compchemeng.2016.04.039 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Large scale optimization of a sour water stripping plant using surrogate models We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA).The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

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“…All of these manipulated variables are considered if they applied for each scheme. The construction of the Pareto front of the azeotropic distillation sequences is made through a multiobjective genetic algorithm with handling constraints, 15 based on the NSGA-II. 26 Note that we have used a population-based method for the design of azeotropic distillation because it offers a better performance for solving multiobjective optimization problems than that obtained with a multiobjective simulated annealing.…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

“…Considering these two sets of parameters, we design a conventional sequence, a side-stream column, and a Petlyuk sequence for the separation of homogeneous azeotropic mixtures. The resulting designs are optimized through a multiobjective genetic algorithm with constraints handling, 15 which is coupled to the Aspen Plus processes simulator; this ensures that all results generated consider the complete model of the distillation sequences. From the Pareto fronts generated, we selected some designs to analyze their theoretical control properties and dynamic performance.…”

Section: Introductionmentioning

confidence: 99%

Effect of Using Adjusted Parameters, Local and Global Optimums, for Phase Equilibrium Prediction on the Synthesis of Azeotropic Distillation Columns

Gutiérrez‐Antonio

Ojeda-Gasca

Bonilla-Petriciolet

et al. 2014

Ind. Eng. Chem. Res.

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ABSTRACT:We analyze the effect of using adjusted parameters, corresponding to local and global optimums, in the NRTL thermodynamic model on the complete process synthesis (design, optimization and control) of homogeneous azeotropic distillation columns. The adjusted parameters that correspond to a global optimum were obtained with simulated annealing technique, while the adjusted parameters that correspond to a local optimum were taken from the Dechema Collection. Both sets of parameters were used to design a conventional sequence, a side-stream column, and a Petlyuk sequence. These designs were used as the initial solution to a multiobjective genetic algorithm with constraints handling, coupled to a processes simulator, where the number of stages and heat duty of each column were considered as objectives; as a result, a set of optimal designs, called the Pareto front, was obtained. Then, we chose some designs to analyze their theoretical control properties and the dynamic performance. Results show remarkable differences in structure, energy consumption, control properties, and dynamic performance of these schemes, depending on the use of adjusted parameters. The results show the importance of using the best adjusted parameters available, which in our case correspond to global optimums obtained with the simulated annealing technique.

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Pareto front of ideal Petlyuk sequences using a multiobjective genetic algorithm with constraints

Cited by 80 publications

References 23 publications

Design and Control of New Intensified Distillation Systems for Quaternary Separations using Genetic Algorithms

Design and Control of New Intensified Distillation Systems for Quaternary Separations using Genetic Algorithms

Large scale optimization of a sour water stripping plant using surrogate models

Effect of Using Adjusted Parameters, Local and Global Optimums, for Phase Equilibrium Prediction on the Synthesis of Azeotropic Distillation Columns

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