Development of a polymer electrolyte fuel cell dead-ended anode purge strategy for use with a nitrogen-containing hydrogen gas supply

Okedi, Tonny I.; Meyer, Quentin; Hunter, Hazel M. A.; Shearing, Paul R.; Brett, Dan J. L.

doi:10.1016/j.ijhydene.2016.11.081

Cited by 25 publications

(5 citation statements)

References 33 publications

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“…Moreover, no CO 2 or CO emissions are created during the decomposition of ammonia to produce hydrogen. The gas produced by this reaction is attractive as a fuel for fuel cells that operate at low temperatures, such as polymer electrolyte fuel cells (PEFCs) because the Pt electrode is easily poisoned by CO. 6,7 Numerous metals, including Ru, 8 Ir, 9 Ni, 10 Rh, 11 Pt, 12 Pd, 13 Co, 14 and Fe, 15 have been demonstrated to act as catalysts for the breakdown of ammonia in earlier studies. Ru is well known for being the most effective catalyst in the group.…”

Section: Introductionmentioning

confidence: 99%

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Ni Nanoparticles Supported on High Surface Area Carborundum for Enhanced Hydrogen Production by Ammonia Decomposition

Tan

Lei

et al. 2023

ACS Appl. Nano Mater.

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Ni nanocatalysts based on high surface area carborundum for the decomposition of ammonia to CO x -free hydrogen are synthesized using a wet impregnation approach. The catalysts were characterized using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and H2-temperature-programmed reduction (TPR) methods. The Ni/SiC catalyst was found to be extremely active for the decomposition reaction when their activities were assessed in a fixed-bed reactor. Ni content and calcination temperature affect the crystallite size, dispersion, and reducibility of Ni species, thereby impacting the catalytic properties of catalysts. The 30Ni/SiC catalyst demonstrates the highest catalytic activity and long-term stability. At a high space velocity of 30 000 mL gcat –1 h–1, the 30Ni/SiC-700 catalyst converts ammonia by 79.6% at 550 °C, demonstrating that Ni/SiC is an effective ammonia decomposition catalyst. The present study provides a promising avenue for the development of cost-effective and high-performance catalysts for hydrogen production via NH3 decomposition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ni Nanoparticles Supported on High Surface Area Carborundum for Enhanced Hydrogen Production by Ammonia Decomposition

Tan

Lei

et al. 2023

ACS Appl. Nano Mater.

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“…As water and nitrogen accumulate and are removed from the anode, hydrogen can reach the active catalyst area and provide sufficient fuel for the electrochemical reaction of the battery, thereby restoring battery performance and minimizing the battery degradation caused by insufficient fuel [20]. Overall, the purging strategies can be classified into the three following categories: (1) fixed purge intervals with different current density [21,22]; (2) slopes of the voltage decrease [14]; and (3) fuel utilization and voltage drop [23]. Sasmito et al [21] adopted a fractional factorial approach to examine the effects of cathode air-stoichiometry, the anode purging period, and the purging duration on the performance of a DEA-PEMFC.…”

Section: Introductionmentioning

confidence: 99%

Construction of Nitrogen Content Observer for Fuel Cell Hydrogen Circuit Based on Anode Recirculation Mode

Wei

Wang³

et al. 2023

WEVJ

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The anode recirculation mode is increasingly being adopted in today’s fuel cell systems. The recycling of hydrogen gas can effectively improve fuel utilization and the wider economy. However, using the purge strategy for the recirculation exhaust has a significant impact on the operational performance and economic efficiency of fuel cell systems.Experiments have shown that, when the purge interval increases from 6 s to 10 s, the recirculation pump power increases by about 20%, the nitrogen content in the exhaust gas increases, and the stack voltage shows a 10 V attenuation. The accumulation of nitrogen permeation in the anode circuit leads to the degradation of the fuel cell performance. Therefore, it is necessary to discharge the accumulated nitrogen through the purge valve in a timely manner. However, opening the exhaust valve with excessively high frequency can result in the unreacted hydrogen being discharged, which reduces the economic efficiency of the fuel cell. This paper is based on the principle of mass conservation and models each subsystem of the anode circuit in the recirculation pump mode of the fuel cell separately, including the proportional valve model, the hydrogen consumption model of the fuel cell, the nitrogen permeation model of the fuel cell, the neural network model of the circulating pump, and the purge valve model. These submodels are integrated to construct a nitrogen content observer for the hydrogen circuit, which can estimate the nitrogen content. The accuracy of the model is validated through experimental data. The estimation error is less than 5.5%. The nitrogen content in the anode circuit can be effectively estimated, providing a model reference for purge operations and improving hydrogen utilization.

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“…25,26 Siegel et al 27 pointed out that accumulation of nitrogen would cause voltage attenuation. Okedi et al 28 conducted experiments with 75% hydrogen and 25% nitrogen, and found that the voltage loss was related to the accumulation of nitrogen, and was more significant under high current density. Uniformity is one of the most important indicators for evaluating the performance of a fuel cell stack, and the "Buckets effect" makes the stack depend on the lowest single-cell voltage 29,30 which can even lead to reverse polarity when the single-cell voltage is too low.…”

Section: Introductionmentioning

confidence: 99%

Methods for estimating the accumulated nitrogen concentration in anode of proton exchange membrane fuel cell stacks based on back propagation neural network

Chen

et al. 2022

Intl J of Energy Research

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Excellent performance is an important indicator to ensure the commercial development of fuel cells. During the operation of a fuel cell system, nitrogen accumulation will occur at the anode of the fuel cell stack because of hydrogen circulation and nitrogen permeation across the membrane. Nitrogen accumulation will lead to the degradation of fuel cell performance. A suitable purge strategy can effectively reduce the excessive nitrogen concentration. The formulation of the purge strategy is closely related to the change in nitrogen concentration, so the accurate estimation of nitrogen concentration is quite important. In this paper, the effect of nitrogen concentration on fuel cell performance under different current densities is studied, and the anode nitrogen concentration in the fuel cell stack is estimated by back propagation neural network. The identification result of nitrogen concentration based on the voltage of every single cell is more accurate. Without considering aging and working condition changes, mean absolute errors of estimated results are 0.75%, 0.67%, 0.62%, and 0.73%, and root mean square errors are 1.09%, 0.97%, 0.88%, and 0.99% at different current densities of 0.6, 0.8, 1.0, and 1.2 AÁcm À2 , respectively. The results indicate that the estimation method of nitrogen concentration for fuel cell anode based on back propagation neural network has a high accuracy, which can provide a new method for formulating purge strategy for fuel cell system.

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Development of a polymer electrolyte fuel cell dead-ended anode purge strategy for use with a nitrogen-containing hydrogen gas supply

Cited by 25 publications

References 33 publications

Ni Nanoparticles Supported on High Surface Area Carborundum for Enhanced Hydrogen Production by Ammonia Decomposition

Ni Nanoparticles Supported on High Surface Area Carborundum for Enhanced Hydrogen Production by Ammonia Decomposition

Construction of Nitrogen Content Observer for Fuel Cell Hydrogen Circuit Based on Anode Recirculation Mode

Methods for estimating the accumulated nitrogen concentration in anode of proton exchange membrane fuel cell stacks based on back propagation neural network

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