An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Kumar, Priya; Gnanasekar, Subashini; Andrews, Nirmala Grace; Rajasekar, N.

doi:10.1049/rpg2.12156

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…Artificial Neural Network was used as robust search algorithm to extract the FC parameters in [28], a slime-mould optimisation algorithm was applied in [13] and a flower pollination algorithm was instead applied in [29].…”

Section: Fuel Cell and Stacksmentioning

confidence: 99%

Modelling of Fuel Cells and Related Energy Conversion Systems

Rossetti¹

2022

ChemEngineering

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Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Various fuel cells have been proposed, of which proton-exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC) are the most frequently used. However, experimental testing rigs are expensive, and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of the operation of fuel cells is compulsory to avoid damage, and such control must be based on accurate models, able to predict cell behaviour and prevent stresses and shutdown. Additionally, when used for mobile applications, intrinsically dynamic operation is needed. Some selected examples of steady-state, dynamic and fluid-dynamic modelling of different types of fuel cells are here proposed, mainly dealing with PEMFC and SOFC types. The general ideas behind the thermodynamic, kinetic and transport description are discussed, with some examples of models derived for single cells, stacks and integrated power cogeneration units. This review can be considered an introductory picture of the modelling methods for these devices, to underline the different approaches and the key aspects to be taken into account. Examples of different scales and multi-scale modelling are also provided.

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Section: Fuel Cell and Stacksmentioning

confidence: 99%

Modelling of Fuel Cells and Related Energy Conversion Systems

Rossetti¹

2022

ChemEngineering

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“…Artificial Neural Network was used as robust search algorithm to extract the FC parameters in [21], a slime mould optimisation algorithm was applied in [7], a flower pollination algorithm was applied in [22].…”

mentioning

confidence: 99%

Multiscale Modelling of Fuel Cells and Related Energy Conversion Systems

Rossetti¹

2022

Preprint

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Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Different fuel cells have been proposed, but experimental testing rigs are expensive and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of operation of fuel cells is compulsory to avoid damage, which must be based on accurate models, able to predict the cell behaviour and prevent stresses and shut-down. Some selected examples of steady state, dynamic and fluid dynamic modelling of different types of fuel cells are proposed, mainly PEMFC and SOFC type. The general ideas behind the thermodynamic, kinetic and transport description are recalled, with some examples of models derived for single cells, stacks and integrated power co-generation units.

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Simulation of novel Pt-M nanocatalysis for proton exchange membrane fuel cells

He,

Bai

2024

AIP Advances

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To enhance proton exchange membrane fuel cells, an ultra-thin cathode catalytic layer based on PtPdCu nanowires is analyzed. The purpose is to optimize fuel cell performance by analyzing key parameters of the catalytic layer in detail, such as thickness and porosity. Numerical simulation methods are used to simulate the structural parameters and operating conditions of the catalytic layer using COMSOL Multi-physics software. The paper focuses on analyzing the changes in the transport resistance of electrons, protons, and oxygen within the catalytic layer, as well as the measurement method of the porosity of the catalytic layer. The results demonstrated that when the catalytic layer thickness reached 450 nm, the power density of proton exchange membrane fuel cells reached its peak, which was 801 and 996 mW/cm2, respectively. In catalytic layers with a thickness of less than 1 µm, the transfer efficiency of oxygen and electrons was higher. When the thickness exceeds 5 µm, oxygen transmission was hindered, and the proton transfer path becomes longer. The average porosity was 44.02%, indicating a high structural consistency of the catalytic layer. In terms of redox reaction performance, the area specific activity of PtPdCuNWs was four times that of commercial Pt/C. This study emphasizes the importance of the ultra-thin cathode catalytic layer in optimizing the performance of proton exchange membrane fuel cells and provides insights into improving catalytic efficiency and overall fuel cell performance through micro-structure design.

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An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Cited by 3 publications

References 49 publications

Modelling of Fuel Cells and Related Energy Conversion Systems

Modelling of Fuel Cells and Related Energy Conversion Systems

Multiscale Modelling of Fuel Cells and Related Energy Conversion Systems

Simulation of novel Pt-M nanocatalysis for proton exchange membrane fuel cells

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