Open AccessMulti‐objective performance optimization of irreversible molten carbonate fuel cell–Stirling heat engine–reverse osmosis and thermodynamic assessment with ecological objective approach
Author(s) -
Ahmadi Mohammad H.,
Sameti Mohammad,
Pourkiaei Seyed M.,
Ming Tingzhen,
Pourfayaz Fathollah,
Chamkha Ali J.,
Oztop Hakan F.,
Jokar Mohammad Ali
Publication year - 2018
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.252
Subject(s) - molten carbonate fuel cell , stirling engine , process engineering , topsis , exergy efficiency , multi objective optimization , exergy , thermal efficiency , desalination , pareto principle , computer science , environmental science , mathematical optimization , mechanical engineering , engineering , mathematics , chemistry , biochemistry , organic chemistry , electrode , operations research , membrane , anode , combustion
This paper aims to investigate a hybrid cycle consisting of a molten carbonate fuel cell (FC) and a Stirling engine which, by connecting to a seawater reverse osmosis desalination unit, provides fresh water. First, a parametric evaluation is performed to study the effect of some key parameters, including the current density and the working temperature of the FC and the thermal conductance between the working substance and the heat reservoirs in the Stirling engine, on the objective functions. The objective functions include the energy efficiency, the exergy destruction rate density, the fresh water production rate, and the ecological function density. After investigating each double combination of these objective functions, two scenarios are defined in quest to concurrently optimize three functions together. The first scenario aims to optimize the energy efficiency, the exergy destruction rate density, and the fresh water production rate; and the second scenario attempts to optimize the energy efficiency, the fresh water production rate, and the ecological function density. A multi‐objective evolutionary algorithm joined with the nondominated sorting genetic algorithm ( NSGA ‐ II ) approach is employed to obtain Pareto fronts in each case scenario. In order to ascertain final solutions between Pareto fronts, three fast and robust decision‐making methods are employed including TOPSIS , LINMAP , and Fuzzy. Finally, a sensitivity analysis is conducted to critically analyze the performance of the system.