Poly[2,2′-(4,4′-bipyridine)-5,5′-bibenzimidazole] functionalization of carbon black for improving the oxidation stability and oxygen reduction reaction of fuel cells

Berber, Mohamed R.; Mustafa, Mohamad Y.

doi:10.1039/d0ra04289g

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The green energy has fascinated many researchers, industrial experts, and scientists to generate electricity and fuel production from natural resources. 1 Strict environmental regulations and subsidies were popularized in most of the countries to develop innovative methods for utilizing nonconventional energy sources, namely wind, solar, wave, and other energy resources. 2 With effective utilization, these renewable energy resources help to meet the required energy demand in recent years.…”

Section: Introductionmentioning

confidence: 99%

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Yousri

Fathy

Babu

et al. 2021

Intl J of Energy Research

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The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm named parasitism-predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work; the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical 5 KW ð Þ dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri-competitive mechanism (MCSO), opposition-based learning competitive PSO (OBLCPSO), ranking-based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e À6 in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e À6 . Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e À2 at 973 K; meanwhile, the most peer (CLPSO) attains 5.44e À1 .

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Section: Introductionmentioning

confidence: 99%

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Yousri

Fathy

Babu

et al. 2021

Intl J of Energy Research

Self Cite

View full text Add to dashboard Cite

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Determination of time constants of diffusion and electrochemical processes in Polymer Electrolyte Membrane Fuel Cells

Iranzo

Navas

Rosa

et al. 2021

Energy

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Poly[2,2′-(4,4′-bipyridine)-5,5′-bibenzimidazole] functionalization of carbon black for improving the oxidation stability and oxygen reduction reaction of fuel cells

Abstract: The rapid oxidation of carbon black (CB) is a major drawback for its use as a catalyst support in polymer electrolyte fuel cells.

Cited by 2 publications

References 31 publications

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Determination of time constants of diffusion and electrochemical processes in Polymer Electrolyte Membrane Fuel Cells

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