Multi-objective optimisation using surrogate models for the design of VPSA systems

Joakim Beck, Daniel Friedrich, Stefano Brandani, Eric S Fraga

Research output: Contribution to journalArticlepeer-review


Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi- objective optimisation method based on kriging surrogate models and NSGA-II with the Active Learning MacKay (ALM) design criterion. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO2/N2 separation. A 5 times reduction in computational effort is observed.
Original languageEnglish
Pages (from-to)318-329
Number of pages12
JournalComputers and Chemical Engineering
Early online date23 Jul 2015
Publication statusPublished - 2 Nov 2015


  • Carbon capture
  • Kriging
  • Multi-objective optimisation
  • Surrogate modelling
  • Vacuum/pressure swing adsorption
  • Dynamic simulation


Dive into the research topics of 'Multi-objective optimisation using surrogate models for the design of VPSA systems'. Together they form a unique fingerprint.

Cite this