A combined method for the design and optimization of intensified distillation systems

Errico, Massimiliano; Pirellas, Pietro; Torres-Ortega, Carlo Edgar; Rong, Ben-Guang; Segovia‐Hernández, Juan Gabriel

doi:10.1016/j.cep.2014.08.005

Cited by 22 publications

(11 citation statements)

References 21 publications

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“…All the configurations are structurally related to the reference used to initialize the procedure. This point proved to be very useful during design and optimization [84,85].…”

Section: Brought To You By | Stockholm University Librarymentioning

confidence: 91%

5. Process synthesis and intensification of hybrid separations

Errico¹

2017

Process Synthesis and Process Intensification

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“…All the configurations are structurally related to the reference used to initialize the procedure. This point proved to be very useful during design and optimization [84,85].…”

Section: Brought To You By | Stockholm University Librarymentioning

confidence: 91%

5. Process synthesis and intensification of hybrid separations

Errico¹

2017

Process Synthesis and Process Intensification

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“…However, in this case, there are difficulties in the operation of the column. The next step is to remove column sections. Errico et al reported that it is possible to remove some intermediate columns formed by a single section without excessive energy penalty and therefore reducing the number of actual columns (Figs. 4 a–c).…”

Section: Methodsmentioning

confidence: 99%

Dynamic Performance of a Complex Distillation System to Separate a Five‐Component Hydrocarbon Mixture

2018

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Distillation is one of the most used separation processes in chemical industry, although it is a highly energy‐intensive operation. For multicomponent distillation, complex structures have been proposed in previous works, which may allow energy savings. Nevertheless, it is mandatory to understand the dynamic characteristics of such complex structures. In this work, the dynamic performance of a dividing‐wall‐based structure for the separation of a five‐component mixture is studied. A sensitivity analysis is performed on the structure in terms of the interlinking streams, performing a singular value decomposition analysis to selected cases with different operational conditions. The designs with the lowest energy duties also showed the best open‐loop properties.

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“…In order to reach this objective, we propose the use of neural networks as surrogate models to estimate both objectives and constraints in the optimization of dividing-wall columns. According to the literature, the optimization problem of dividing-wall columns has been addressed by several researchers using different strategies as genetic algorithms 33,36,40,45,48 , differential evolution 52,53 , simulated annealing 54,55 , respon se surface methodology 56 , mathematical programming 57-61 using complete models or shortcut methodologies. However, the alternate use of surrogate models with the complete model in Aspen Plus has not been proposed.…”

Section: Dividing-wall Columnsmentioning

confidence: 99%

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Gutiérrez‐Antonio

Briones-Ramírez²

2016

Chem. Biochem. Eng. Q.

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Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front.

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A combined method for the design and optimization of intensified distillation systems

Cited by 22 publications

References 21 publications

5. Process synthesis and intensification of hybrid separations

5. Process synthesis and intensification of hybrid separations

Dynamic Performance of a Complex Distillation System to Separate a Five‐Component Hydrocarbon Mixture

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

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