4.7 Article

Exergy analysis of a polymer fuel cell and identification of its optimum operating conditions using improved Farmland Fertility Optimization

Journal

ENERGY
Volume 216, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119264

Keywords

Proton exchange membrane fuel cell; Exergy; Work; Irreversibility; Improved farmland fertility optimization

Funding

  1. National Natural Science Foundation of China [51979078]
  2. Fundamental Research Funds for Central Universities

Ask authors/readers for more resources

This paper presents an exergy analysis of a power generation system based on high-temperature proton exchange membrane fuel cells, which includes a complex mathematical model and parameter analysis to optimize the system for improved efficiency and performance. The proposed optimization algorithm, Farmland Fertility Optimization (FFO), outperforms other compared algorithms in terms of irreversibility, exergy efficiency, and work.
In this paper, the exergy analysis of a proposed power generation system based on high-temperature proton exchange membrane fuel cell (HT-PEMFC) has been proposed. The analyzed system includes the organic Rankine cycle to recover the wasted heat. To analyze the system, a mathematical model of the considered PEMFC has been presented and the water management system is investigated. Parametric analysis is provided to investigate the effect of the various thermodynamic and economic parameters such as exergy, irreversibility, and the work. Therefore, the three mentioned parameters have been optimized for improving the PEMFC design. To achieve an efficient optimized system, a new design of Farmland Fertility Optimization (FFO) is proposed. Final results of the algorithm are compared with experimental results, the genetic algorithm, and the basic FFO and the optimized values of irreversibility, exergy efficiency, work for the proposed algorithm are achieved 0.011,-0.446, and -0.462, respectively that are the best results toward the other compared algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available