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Optimal Design of Distillation Columns Using a Combined Optimisation Approach

Chia, DN; Duanmu, F; Sorensen, E; (2021) Optimal Design of Distillation Columns Using a Combined Optimisation Approach. In: Computer Aided Chemical Engineering. (pp. 153-158). Elsevier

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Abstract

Distillation is the most important separation process in the chemical industry despite being very energy-intensive, hence any attempt to optimise its design may potentially have a significant impact. In this work, a combined optimisation strategy based on Genetic Algorithm (GA, stochastic method) and an outer approximation method (OAERAP, deterministic method) is proposed and demonstrated to reliably and efficiently optimise different distillation processes by achieving significant energy and/or capital savings. Three different case studies are presented to compare the combined strategy to the stand-alone GA and OAERAP methods. The combined optimisation strategy shows excellent results, with the total annualised costs obtained from the combined strategy being similar or better than for the stand-alone methods. More importantly, the combined strategy requires much shorter CPU time, as well as significantly less manual effort, thus greatly increasing the time efficiency when compared to the OAERAP method.

Type: Book chapter
Title: Optimal Design of Distillation Columns Using a Combined Optimisation Approach
DOI: 10.1016/B978-0-323-88506-5.50025-5
Publisher version: https://doi.org/10.1016/B978-0-323-88506-5.50025-5
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Optimisation, Genetic Algorithm, Outer-approximation, Distillation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10139687
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