Cristin-resultat-ID: 2041204
Sist endret: 8. februar 2023, 13:15
NVI-rapporteringsår: 2022
Resultat
Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
2022

Bayesian Optimization for techno-economic analysis of pressure swing adsorption processes

Bidragsytere:
  • Leif Erik Andersson
  • Johannes Schilling
  • Luca Riboldi
  • André Bardow og
  • Rahul Anantharaman

Bok

32nd European Symposium on Computer Aided Process Engineering
ISBN:
  • 9780323958790

Utgiver

Elsevier
NVI-nivå 1

Serie

Computer-aided chemical engineering
ISSN 1570-7946
NVI-nivå 1

Om resultatet

Vitenskapelig Kapittel/Artikkel/Konferanseartikkel
Publiseringsår: 2022
Volum: 51
Hefte: 51
Sider: 1441 - 1446
ISBN:
  • 9780323958790

Klassifisering

Fagfelt (NPI)

Fagfelt: Kjemi og materialteknologi
- Fagområde: Realfag og teknologi

Beskrivelse Beskrivelse

Tittel

Bayesian Optimization for techno-economic analysis of pressure swing adsorption processes

Sammendrag

Pressure swing adsorption (PSA) can remove CO2 from flue gases. The full potential of the technology can only be exploited if the optimal combination of adsorbent material and process is identified. This identification requires screening the large database of adsorbent materials by performing computationally intensive process optimizations. To ensure a suitable compromise between accuracy and computational lightness, the PSA process can be described by reduced-order models. However, those models might involve several discrete states making the objective function discontinuous and not continuously differentiable, challenging gradient-based methods. The selection of suitable optimization methods is therefore an open issue. This study compares three optimization algorithms, Bayesian optimization, NOMAD and KNITRO, for two cases and adsorbent materials. For the tested cases and adsorbent materials, none of the three algorithms is clearly superior to the other. Bayesian optimization (BO) needs fewest function evaluations and computational time to converge and outperforms the other two for one case. However, BO is less reliable than NOMAD and KNITRO for the other case tested.

Bidragsytere

Leif Erik Andersson

  • Tilknyttet:
    Forfatter
    ved Gassteknologi ved SINTEF Energi AS

Johannes Schilling

  • Tilknyttet:
    Forfatter
    ved Eidgenössische Technische Hochschule Zürich

Luca Riboldi

  • Tilknyttet:
    Forfatter
    ved Gassteknologi ved SINTEF Energi AS

André Bardow

  • Tilknyttet:
    Forfatter
    ved Forschungszentrum Jülich
  • Tilknyttet:
    Forfatter
    ved Eidgenössische Technische Hochschule Zürich

Rahul Anantharaman

  • Tilknyttet:
    Forfatter
    ved Gassteknologi ved SINTEF Energi AS
1 - 5 av 5

Resultatet er en del av Resultatet er en del av

32nd European Symposium on Computer Aided Process Engineering.

Montastruc, Ludovic; Negny, Stéphane. 2022, Elsevier. TLSE3Vitenskapelig antologi/Konferanseserie
1 - 1 av 1