A novel solar combined cycle integration: An exergy-based optimization using artificial neural network

Ahmad M. Abubaker, Adnan Darwish Ahmad, Ahmad A. Salaimeh, Nelson K. Akafuah, Kozo Saito

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The paper aims to solve drawbacks associated with Gas Turbine GT by integrating a novel cascaded system into a combined cycle simultaneously. Parabolic trough collectors preheat the air at the combustion chamber inlet, then drive an absorption inlet-air cooling cycle that controls the ambient-air temperature at the compressor's inlet. This study uses the 2nd law of thermodynamics to estimate the maximum available energy, calculate the electric exergy efficiency, and explore the maximum irreversible exergy destruction in the system's components. Artificial Neural Network was employed to develop a multi-objective optimization by linking data collected from equations in the Engineering Equation Solver software with Matlab. Spider diagrams investigated the effect of varying several key operating parameters on the performance of the system, identifying gas turbine as the highest irreversibility sub-unit and the solar field parabolic trough collectors as the second. Design improvement for the combustion chamber can reduce 303.6 MW, and for parabolic trough collectors field reduce 58.9 MW. Artificial neural networks with multi-objective optimization maximized the electric exergy destruction to 46.19% and minimized the exergy destruction to 489.4 MW, relative to the corresponding values from the simple design point.

Original languageEnglish
Pages (from-to)914-932
Number of pages19
JournalRenewable Energy
Volume181
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
The authors would like to acknowledge Eng. Hussain Sharadga for his guidance with the ANN, Eng. Loiy AlGhussain for his valuable help with the optimization analysis and solar data, and the University of Kentucky's IR4TD members for their useful comments and discussions.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Artificial neural network
  • Combined cycle
  • Exergy
  • Inlet air cooling
  • Parabolic trough collectors
  • Solar power

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Fingerprint

Dive into the research topics of 'A novel solar combined cycle integration: An exergy-based optimization using artificial neural network'. Together they form a unique fingerprint.

Cite this