Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

Bozorgmehri, Shahriar; Hamedi, Mohsen

doi:10.1002/fuce.201100140

Cited by 56 publications

(21 citation statements)

References 27 publications

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“…The result suggests that the TPB length goes through a familiar curve that features a maximum at a low infiltration loading (about 4‐7%), agreeing with our studies . The complexity of investigation of parametric study leads researchers to couple theoretical models with artificial intelligence approach, such as artificial neural network and genetic algorithm . For example, after generating 3D microstructures of infiltrated electrodes, Tafazoli et al build a search engine with artificial intelligence approach to find the optimal geometric properties …”

Section: Introductionsupporting

confidence: 84%

“…26,28 The complexity of investigation of parametric study leads researchers to couple theoretical models with artificial intelligence approach, such as artificial neural network 32,33 and genetic algorithm. 34 For example, after generating 3D microstructures of infiltrated electrodes, Tafazoli et al build a search engine with artificial intelligence approach to find the optimal geometric properties. 35 In this study, we focus on the third type of infiltrated electrodes with dual-phase backbone and conduct a parametric study to evaluate the effects of backbone volume ratio, infiltrated nanoparticle radius, and aggregation risk factor on the geometric properties.…”

Section: Introductionmentioning

confidence: 99%

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A numerical study of infiltrated solid oxide fuel cell electrode with dual‐phase backbone

2018

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Summary Three‐dimensional microstructure of infiltrated solid oxide fuel cell electrodes with dual‐phase backbone is simulated numerically. This work employs LSM (lanthanum strontium manganite) infiltrated LSM yttria‐stabilized zirconia composite electrodes as an example. Important geometric properties, including percolation probability of LSM, total and percolated three‐phase boundary (TPB) lengths, and total and percolated surface areas of LSM, are calculated under various LSM nanoparticle loadings. One important finding is that compared with pure yttria‐stabilized zirconia backbone, dual‐phase backbone results in lower TPB length and has little impact on the surface area of LSM particles. Therefore, the addition of LSM backbone may be ineffective, even negative in promoting the electrode performance, however, can significantly reduce the LSM infiltration threshold loading to form a percolating network. The trade‐off between decreasing infiltration cycle and increasing TPB length demands an optimized content of LSM in backbone.

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Section: Introductionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A numerical study of infiltrated solid oxide fuel cell electrode with dual‐phase backbone

2018

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“…Structure of the artificial neural network (ANN) used by Bozorgmehri and Hamedi to model the IT‐SOFC [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Theorymentioning

confidence: 99%

“…Bozorgmehri and Hamedi used an ANN and genetic algorithm (GA) to model and optimize the parameters of a single medium‐temperature fuel cell SOFC (IT‐SOFC). The ANN model was based on a one‐way network, which uses Levenberg‐Marquardt–based retrograde error propagation.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network model of molten carbonate fuel cells: Validation on experimental data

et al. 2019

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Summary This article shows the teaching processes of artificial neural networks that are used to model the molten carbonate fuel cell (MCFC). Researchers model MCFCs to address a variety of issues across a range of complexities, from simply gauging the effect of temperature through to a complete model with 14 input parameters. The architecture of the model is a triple layer network with one hidden layer containing three neurons. The activation function used for the hidden layer was a hyperbolic tangent, with the last layer being based on linear function. We produced various network configurations, mostly networks containing one hidden layer. Models map the work of a real fuel cell with an average error in the range of 2.4% to 4.6%. The model we created guided the optimization of the thermal‐flow and construction parameters of the MCFC. Commercially available software was used to build the model and optimize the operating parameters. The selected objective functions were the efficiency of electricity production and the power density obtained from the cell's surface. The results obtained serve as pointers for possible changes in fuel cell operation and could lead to some structural changes being made.

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“…Mathematical modeling is an essential tool for designing fuel cell systems which represents the important aspects of the existing system and presents knowledge of that system in a useful manner. There have been several publications, studying the effect of different variables and their sensitivity on the SOFC performance [4][5][6][7][8]59]. The reported works described SOFC's steady-state or dynamic performances.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a Tubular‐SOFC: The Effect of the Thermal Radiation of Fuel Components and CO Participating in the Electrochemical Process

et al. 2012

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A mathematical model based on first principles is developed to study the effect of heat and electrochemical phenomena on a tubul solid oxide fuel cell (SOFC). The model accounts fordiffusion, inherent impedance, transport (momentum, heat and mass transfer) processes, internal reforming/shifting reaction, electrochemical processes, and potential losses (activation, concentration, and ohmic losses). Thermal radiation of fuel gaseous components is considered in detail in this work in contrast to other reported work in the literature. The effect of thermal radiation on SOFC performance is shown by comparing with a model without this factor. Simulation results indicate that at higher inlet fuel flow pressures and also larger SOFC lengths the effect of thermal radiation on SOFC temperature becomes more significant. In this study, the H2 and CO oxidation is also studied and the effect of CO oxidation on SOFC performance is reported. The results show that the model which accounts for the electrochemical reaction ofCO results in better SOFC performance than other reported models. This work also reveals that at low inlet fuel flow pressures the CO and H2 electrochemical reactions are competitive and significantly dependent on the CO/H2 ratio inside the triple phase boundary.

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Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

Cited by 56 publications

References 27 publications

A numerical study of infiltrated solid oxide fuel cell electrode with dual‐phase backbone

A numerical study of infiltrated solid oxide fuel cell electrode with dual‐phase backbone

Artificial neural network model of molten carbonate fuel cells: Validation on experimental data

Modeling of a Tubular‐SOFC: The Effect of the Thermal Radiation of Fuel Components and CO Participating in the Electrochemical Process

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