Detail publikace

Parametric optimization of steam turbine networks by using commercial simulation software

Li, Ai-Hong Ren, Xiao-Ying Varbanov, Petar Liu, Zhi-Yong

Originální název

Parametric optimization of steam turbine networks by using commercial simulation software

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

The steam turbine network can significantly affect the energy utilization efficiency of a chemical plant. The parametric optimization of steam turbine network is investigated in this paper by using Aspen Plus software. The existing steam turbine network is simulated first. Then, for minimizing the operating cost of the steam system, the network parameters including the steam production of boilers, the steam exhaust of turbines, the steam passing through let-down valves and the steam allocation among different turbines are optimized based on the "Sensitivity Analysis" tool and the proposed optimization rules. The investigation of two literature examples shows that the proposed method is simple and can provide the results comparable to those obtained by mathematical programming methods.

Klíčová slova

Parametric optimization; steam turbine networks; simulation software

Autoři

Li, Ai-Hong; Ren, Xiao-Ying; Varbanov, Petar; Liu, Zhi-Yong

Vydáno

1. 8. 2022

Nakladatel

Esevier

Místo

Amsterdam

ISSN

0263-8762

Periodikum

CHEMICAL ENGINEERING RESEARCH & DESIGN

Číslo

184

Stát

Spojené království Velké Británie a Severního Irska

Strany od

246

Strany do

255

Strany počet

10

URL

https://www.webofscience.com/wos/woscc/summary/0e41d00f-43af-45c9-8e7e-70b84cf3950f-6f85e65f/relevance/1

BibTex

@article{BUT182637,
  author="Li, Ai-Hong and Ren, Xiao-Ying and Varbanov, Petar and Liu, Zhi-Yong",
  title="Parametric optimization of steam turbine networks by using commercial simulation software",
  journal="CHEMICAL ENGINEERING RESEARCH & DESIGN",
  year="2022",
  number="184",
  pages="246--255",
  doi="10.1016/j.cherd.2022.06.0140263-8762",
  issn="0263-8762",
  url="https://www.webofscience.com/wos/woscc/summary/0e41d00f-43af-45c9-8e7e-70b84cf3950f-6f85e65f/relevance/1"
}