Title

Pareto-Based Multi-Objective Optimization Of Recuperated S-Co2 Brayton Cycles

Keywords

Brayton cycle; Genetic algorithm; Multi-objective optimization; Supercritical carbon dioxide

Abstract

In this paper, two configurations of the S-CO2 Brayton cycles (i.e., the single-recuperated and recompression cycles) are thermodynamically modeled and optimized through a multi-objective approach. Two semi-conflicting objectives, i.e., cycle efficiency (ηc) and cycle specific power (Φsp) are maximized simultaneously to achieve Pareto optimal fronts. The objective of maximum cycle efficiency is to have a smaller and less expensive solar field, and a lower fuel cost in case of a hybrid scheme. On the other hand, the objective of maximum specific power provides a smaller power block, and a lower capital cost associated with recuperators and coolers. The multi-objective optimization is carried out by means of a genetic algorithm which is a robust method for multidimensional, nonlinear system optimization. The optimization process is comprehensive, i.e., all the decision variables including the inlet temperatures and pressures of turbines and compressors, the pinch point temperature differences, and the mass flow fraction of the main compressor are optimized simultaneously. The presented Pareto optimal fronts provide two optimum trade-off curves enabling decision makers to choose their desired compromise between the objectives, and to avoid naive solution points obtained from a single-objective optimization approach. Moreover, the comparison of the Pareto optimal fronts associated with the studied configurations reveals the optimum operational region of the recompression configuration where it presents superior performance over the single-recuperated cycle.

Publication Date

1-1-2014

Publication Title

Proceedings of the ASME Turbo Expo

Volume

3B

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/GT2014-27152

Socpus ID

84922377423 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84922377423

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