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 and Kozo Saito

Renewable Energy, 2022, vol. 181, issue C, 914-932

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.

Keywords: Exergy; Solar power; Parabolic trough collectors; Combined cycle; Artificial neural network; Inlet air cooling (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:181:y:2022:i:c:p:914-932

DOI: 10.1016/j.renene.2021.09.087

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