Multi-objective optimization of a direct methanol fuel cell system using a genetic-based algorithm

Mert, Suha Orçun; Özçelik, Zehra

doi:10.1002/er.2963

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…The exergy analysis in fuel cell shows that the cost of generated power can be achieved by the exergy value, that is, exergy is a suitable economical criterion for analyzing of the system. The exergy is a function related to the work, irreversibility, entropy, and enthalpy of a system . This paper studies the details of the exergy evaluation and minimizing it by three different functions based on some predefined constraints and an improved collective animal behavior (ICAB) algorithm.…”

Section: Modeling Of the Systemmentioning

confidence: 99%

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Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Han

Khodaei

2020

Int J Energy Res

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Summary The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

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Section: Modeling Of the Systemmentioning

confidence: 99%

“…In addition, reversible heat has a dependency on the entropy of reaction, cell operating temperature, and the current of reaction are considered as follows:

{\dot{Q}}_{rev} = ((), - T_{FC} ∆ S_{T}) \times \frac{i}{F \times r_{f}}

where, ∆S T describes the entropy change as follows

∆ S_{T} = 12414.8 - 9967.35 \times \ln T_{FC}

…”

Section: Modeling Of the Systemmentioning

confidence: 99%

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Han

Khodaei

2020

Int J Energy Res

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“…Exergoeconomic analysis aims at the following 26,27 : -to identify the location, magnitude, and sources of thermodynamic losses, -to calculate the cost associated with exergetic losses and destroyed exergy in any system component, -to analyse the cost formation of each subsystem and product separately.…”

Section: ( ) ( )mentioning

confidence: 99%

Exergoeconomic Distillation Sequencing by Multi-objective Optimization through a Hybrid Genetic Algorithm

Özçelik

Mert

2016

Chem.Biochem.Eng.Q.

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While trying to optimize sharp distillation processes, the number of possible column sequences significantly increases as the number of components that make up the feed mixture increases. As a result, proper sequencing with maximum exergetic profit and minimum exergy destruction becomes harder to achieve. In this study, an exergoeconomic multi-objective optimization was applied to the distillation sequences of three separate hydrocarbon mixture cases, by means of a genetic-algorithm-based solver software. A computer program (DISMO) was developed in-house to achieve this functionality. The results indicate that the created algorithm was quite applicable in determining the optimum sequencing in distillation, as it successfully created the Pareto Solution Set and suggested the optimum configurations. This study also presented an opportunity to conduct a parametric investigation on various weighting factors for objective functions. As the importance given to a specific objective was increased, the optimization results had a tendency to favour that specific objective through arrangement of sequencing as expected, though the profit and sequencing converged to a single result after a certain threshold.

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“…Pourmirzaagha et al designed a new type of battery cell using the particle swarm optimization, and both single‐objective and multiobjective optimization methods were used to find the optimal battery thickness and energy. Mert et al used an improved genetic algorithm to analyze three important performance indicators of fuel cells: power, energy, and exergy. Sadeghi et al compared the working fluids of different kinds of ORCs through multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

Zhang

Pan

et al. 2019

Int J Energy Res

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Summary In this paper, a novel cogeneration system integrating Kalina cycle, CO2 chemical absorption, process, and flash‐binary cycle is proposed to remove acid gases in the exhaust gas of solid oxide fuel cell (SOFC) system, improve the waste heat utilization, and reduce the cold energy consumed during CO2 capture. In the CO2 chemical absorption process, the methyldiethanolamine (MDEA) aqueous solution is utilized as a solvent, and feed temperature and absorber pressure are optimized via Aspen Plus software. The single‐objective and multiobjective optimization are carried out for the flash‐binary cycle subsystem. Results show that when the multiobjective optimization is applied to identify the exergoeconomic condition, the cogeneration system can simultaneously satisfy the high thermodynamic cycle efficiency and also the low product unit cost. The optimal results of the exergy efficiency, product unit cost, and normalized CO2 emissions obtained by Pareto chart were 75.84%, 3.248 $/GJ, and 13.14 kg/MWhr, respectively.

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Multi-objective optimization of a direct methanol fuel cell system using a genetic-based algorithm

Cited by 15 publications

References 16 publications

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Exergoeconomic Distillation Sequencing by Multi-objective Optimization through a Hybrid Genetic Algorithm

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

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