A basic model for analysis of molten carbonate fuel cell behavior

Baranak, Murat; Atakül, Hüsnü

doi:10.1016/j.jpowsour.2007.05.027

Cited by 84 publications

(24 citation statements)

References 19 publications

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“…The complexity of the models varies from a lumped representation of the fuel cell stack [5,6] over the simulation of a small 3D cutout section of one cell [7] to models of one fuel cell, taking into account the 3D geometry [8] or a simplified 2D representation of the cell plane [9][10][11][12][13]]. An agglomerate model where the volume averaged stack is simulated was presented by Brouwer et al [14].…”

Section: Introductionmentioning

confidence: 99%

Stack Modelling of a Molten Carbonate Fuel Cell (MCFC)

2010

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A model of a molten carbonate fuel cell (MCFC) stack including internal steam reforming is presented. It comprises a symmetric section of the stack, consisting of one half indirect internal reforming unit (IIR) and four fuel cells. The model describes the gas phase compositions, the gas and solid temperatures and the current density distribution within the highly integrated system. The model assumptions, the differential equations and boundary conditions as well as the coupling equations used in the model are shown. The strategy to solve the system of partial differential equations is outlined. The simulation results show that the fuel cells within the stack operate at different temperatures. This is expected to have an impact on the voltages as well as the degradation rates within the individual fuel cells. © 2010 WILEY-VCH Verlag GmbH & Co. KGaA,Weinheim [accessed September 2nd, 2010

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Section: Introductionmentioning

confidence: 99%

Stack Modelling of a Molten Carbonate Fuel Cell (MCFC)

2010

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“…The variation in the chemical composition at the anode and cathode side is driven by the current generation, through Faraday's law, and by the operating temperature and the partial pressure of the constituents, through an equilibrium model, which is expressed by [10] K PS ¼ 10 5:47Â10 À12 $T ˇ 4 À2:57Â10 À8 $T ˇ 3 À4:64Â10 À5 $T ˇ 2 À3:92Â10 À2 $Tþ13:21…”

Section: System Modelmentioning

confidence: 99%

Lifetime optimization of a molten carbonate fuel cell power system coupled with hydrogen production

Nicolin

Verda

2011

Energy

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“…Researchers have published papers on a large number of mathematical models of MCFCs . An MCFC model should factor in electrochemistry, heat, mass, and charge transfer processes.…”

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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A basic model for analysis of molten carbonate fuel cell behavior

Cited by 84 publications

References 19 publications

Stack Modelling of a Molten Carbonate Fuel Cell (MCFC)

Stack Modelling of a Molten Carbonate Fuel Cell (MCFC)

Lifetime optimization of a molten carbonate fuel cell power system coupled with hydrogen production

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

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