scholarly journals The Effect of Cell Compression and Cathode Pressure on Hydrogen Crossover in PEM Water Electrolysis

2021 ◽  
Author(s):  
Agate Martin ◽  
Patrick Trinke ◽  
Markus Stähler ◽  
Andrea Stähler ◽  
Fabian Scheepers ◽  
...  
Keyword(s):  
Mass Transport ◽  
Cell Voltage ◽  
Water Electrolysis ◽  
Material Design ◽  
Operating Conditions ◽  
Cell Performance ◽  
Membrane Electrode ◽  
Current Densities ◽  
Hydrogen Crossover ◽  
The Impact

Abstract Hydrogen crossover poses a crucial issue for polymer electrolyte membrane (PEM) water electrolysers in terms of safe operation and efficiency losses, especially at increased hydrogen pressures. Besides the impact of external operating conditions, the structural properties of the materials also influence the mass transport within the cell. In this study, we provide an analysis of the effect of elevated cathode pressures (up to 15 bar) in addition to increased compression of the membrane electrode assembly on hydrogen crossover and the cell performance, using thin Nafion 212 membranes and current densities up to 3.6 A cm-2. It is shown that a higher compression leads to increased mass transport overpotentials, although the overall cell performance is improved due to the decreased ohmic losses. The mass transport limitations also become visible in enhanced anodic hydrogen contents with increasing compression at high current densities. Moreover, increases in cathode pressure are amplifying the compression effect on hydrogen crossover and mass transport losses. The results indicate that the cell voltage should not be the only criterion for optimizing the system design, but that the material design has to be considered for the reduction of hydrogen crossover in PEM water electrolysis.

Effect of Operating Parameters on the DMFC Performance

2004 ◽  
Vol 2 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Guo-Bin Jung ◽  
Ay Su ◽  
Cheng-Hsin Tu ◽  
Fang-Bor Weng
Keyword(s):  
Concentration Polarization ◽  
Solution Concentration ◽  
Open Circuit ◽  
Methanol Solution ◽  
Operating Conditions ◽  
Cell Performance ◽  
Membrane Electrode ◽  
Membrane Thickness ◽  
Methanol Crossover ◽  
Cell Temperature

Methanol crossover largely affects the efficiency of power generation in the direct methanol fuel cell. As the methanol crosses over through the membrane, the methanol oxidizes at the cathode, resulting in low fuel utilization and in a serious overpotential loss. In this study, the commercial membrane electrode assemblies (MEAs) are investigated with different operating conditions such as membrane thickness, cell temperature, and methanol solution concentration. The effects of these parameters on methanol crossover and power density are studied. With the same membrane, increasing the cell temperature promotes the cell performance as expected, and the lower methanol concentration causes the concentration polarization effects, thus resulting in lower cell performance. Although higher methanol solution concentration can overcome the concentration polarization, a serious methanol crossover decreases the cell performance at high cell temperature. In this study, the open circuit voltage (OCV) is inversely proportional to methanol solution concentration, and is proportional to membrane thickness and cell temperature. Although increasing membrane thickness lowers the degree of methanol crossover, on the other hand, the ohmic resistance increases simultaneously. Therefore, the cell performance using Nafion 117 as membrane is lower than that of Nafion 112. In addition, the performance of the MEA made in our laboratory is higher than the commercial product, indicating the capability of manufacturing MEA is acceptable.

Testing of solid oxide cells at high current densities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
André Weber
Keyword(s):  
Single Cell ◽  
High Performance ◽  
High Efficiency ◽  
Electrochemical Impedance ◽  
Single Cells ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Current Densities ◽  
Cell Testing ◽  
The Impact

Abstract Solid Oxide Cells (SOCs) have gained an increasing interest as electrochemical energy converters due to their high efficiency, fuel flexibility and ability of reversible fuel cell/electrolysis operation. During the development process as well as in quality assurance tests, the performance of single cells and cell stacks is commonly evaluated by means of current/voltage- (CV-) characteristics. Despite of the fact that the measurement of a CV-characteristic seems to be simple compared to more complex, dynamic methods as electrochemical impedance spectroscopy or current interrupt techniques, the resulting performance strongly depends on the test setup and the chosen operating conditions. In this paper, the impact of different single cell testing environments and operating conditions on the CV-characteristic of high performance cells is discussed. The influence of cell size, contacting and current collection, contact pressure, fuel flow rate and composition on the achievable cell performance is presented and limitations arising from the test bed and testing conditions will be pointed out. As today’s high performance cells are capable of delivering current densities of several ampere per cm2 a special emphasis will be laid on single cell testing in this current range.

Dispersion controlled by permeable surfaces: surface properties and scaling

2016 ◽  
Vol 801 ◽  
pp. 13-42 ◽  
Author(s):  
Bowen Ling ◽  
Alexandre M. Tartakovsky ◽  
Ilenia Battiato
Keyword(s):  
Mass Transport ◽  
Solute Transport ◽  
Surface Properties ◽  
Peclet Number ◽  
Dispersion Coefficient ◽  
Operating Conditions ◽  
Péclet Number ◽  
Porosity And Permeability ◽  
Matrix Porosity ◽  
The Impact

Permeable and porous surfaces are common in natural and engineered systems. Flow and transport above such surfaces are significantly affected by the surface properties, e.g. matrix porosity and permeability. However, the relationship between such properties and macroscopic solute transport is largely unknown. In this work, we focus on mass transport in a two-dimensional channel with permeable porous walls under fully developed laminar flow conditions. By means of perturbation theory and asymptotic analysis, we derive the set of upscaled equations describing mass transport in the coupled channel–porous-matrix system and an analytical expression relating the dispersion coefficient with the properties of the surface, namely porosity and permeability. Our analysis shows that their impact on the dispersion coefficient strongly depends on the magnitude of the Péclet number, i.e. on the interplay between diffusive and advective mass transport. Additionally, we demonstrate different scaling behaviours of the dispersion coefficient for thin or thick porous matrices. Our analysis shows the possibility of controlling the dispersion coefficient, i.e. transverse mixing, by either active (i.e. changing the operating conditions) or passive mechanisms (i.e. controlling matrix effective properties) for a given Péclet number. By elucidating the impact of matrix porosity and permeability on solute transport, our upscaled model lays the foundation for the improved understanding, control and design of microporous coatings with targeted macroscopic transport features.

scholarly journals PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 434 ◽  
Author(s):  
Andrés Morán-Durán ◽  
Albino Martínez-Sibaja ◽  
José Pastor Rodríguez-Jarquin ◽  
Rubén Posada-Gómez ◽  
Oscar Sandoval González
Keyword(s):  
Fuel Cell ◽  
Output Voltage ◽  
Neural Control ◽  
Principal Component ◽  
Cell Voltage ◽  
Cell Performance ◽  
Fuel Cell Performance ◽  
Model Control ◽  
And Control ◽  
The Impact

Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

scholarly journals Experimental Study on the Effect of Hydrogen Sulfide on High-Temperature Proton Exchange Membrane Fuel Cells by Using Electrochemical Impedance Spectroscopy

Catalysts ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 441 ◽  
Author(s):  
Ren-Jun Kang ◽  
Yong-Song Chen
Keyword(s):  
Hydrogen Sulfide ◽  
High Temperature ◽  
Proton Exchange Membrane ◽  
Electrochemical Impedance ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Cell Performance ◽  
Membrane Electrode ◽  
Pure Hydrogen ◽  
Exchange Membrane

When the fuel supplied to a high-temperature proton exchange membrane fuel cell (HT-PEMFC) is produced by hydrocarbon formation, hydrogen sulfide (H2S) may appear, resulting in decreased cell performance and durability. To study the effects of H2S on the performance and durability of the HT-PEMFC, a series of experiments was conducted. In the first step, the effects of polyvinylidene fluoride (PVDF) and platinum loading on cell performance were investigated and discussed under pure hydrogen operation conditions. Optimal PVDF and platinum compositions in the catalyst layer are suggested. Then, the effect of H2S on membrane electrode assembly (MEA) performance with various platinum loadings was investigated by supplying hydrogen containing 5.2 ppm of H2S to the anode of the MEA. An electrochemical impedance spectroscope was employed to measure the impedance of the MEAs under various operating conditions. Finally, degradation of the MEA when supplied with hydrogen containing 5.2 ppm of H2S was analyzed and discussed. The results suggest that the performance of an MEA with 0.7 mg Pt cm−2 and 10% PVDF can be recovered by supplying pure hydrogen. The rate of voltage decrease is around 300 μV h−1 in the presence of H2S.

scholarly journals Measurement of polarization curve and development of a unique semi empirical model for description of PEMFC and DMFC performances

2011 ◽  
Vol 17 (2) ◽  
pp. 207-214 ◽  
Author(s):  
T. Selyari ◽  
A.A. Ghoreyshi ◽  
M. Shakeri ◽  
G.D. Najafpour ◽  
T. Jafary
Keyword(s):  
Experimental Data ◽  
Fuel Cells ◽  
Fuel Cell ◽  
Current Density ◽  
Power Output ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Cell Performance ◽  
Oxygen Stoichiometry ◽  
Semi Empirical

In this study, a single polymer electrolyte membrane fuel cell (PEMFC) in H2/O2 form with an effective dimension of 5?5 cm as well as a single direct methanol fuel cell (DMFC) with a dimension of 10?10 cm were fabricated. In an existing test station, the voltage-current density performances of the fabricated PEMFC and DMFC were examined under various operating conditions. As was expected DMFC showed a lower electrical performance which can be attributed to the slower methanol oxidation rate in comparison to the hydrogen oxidation. The results obtained from the cell operation indicated that the temperature has a great effect on the cell performance. At 60?C, the best power output was obtained for PEMFC. There was a drop in the cell voltage beyond 60?C which can be attributed to the reduction of water content inside the membrane. For DMFC, maximum power output was resulted at 64oC. Increasing oxygen stoichiometry and total cell pressure had a marginal effect on the cell performance. The results also revealed that the cell performance improved by increasing pressure differences between anode and cathode. A unified semi-empirical thermodynamic based model was developed to describe the cell voltage as a function of current density for both kinds of fuel cells. The model equation parameters were obtained through a nonlinear fit to the experimental data. There was a good agreement between the experimental data and the model predicted cell performance for both types of fuel cells.

scholarly journals Methanol Electrolysis for Hydrogen Production Using Polymer Electrolyte Membrane: A Mini-Review

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5879
Author(s):  
Sethu Sundar Pethaiah ◽  
Kishor Kumar Sadasivuni ◽  
Arunkumar Jayakumar ◽  
Deepalekshmi Ponnamma ◽  
Chandra Sekhar Tiwary ◽  
...  
Keyword(s):  
Polymer Electrolyte ◽  
Polymer Electrolyte Membrane ◽  
Pem Fuel Cells ◽  
H2 Production ◽  
Operating Conditions ◽  
Catalyst Loading ◽  
Membrane Electrode ◽  
Membrane Thickness ◽  
Electrolyte Membrane ◽  
The Impact

Hydrogen (H2) has attained significant benefits as an energy carrier due to its gross calorific value (GCV) and inherently clean operation. Thus, hydrogen as a fuel can lead to global sustainability. Conventional H2 production is predominantly through fossil fuels, and electrolysis is now identified to be most promising for H2 generation. This review describes the recent state of the art and challenges on ultra-pure H2 production through methanol electrolysis that incorporate polymer electrolyte membrane (PEM). It also discusses about the methanol electrochemical reforming catalysts as well as the impact of this process via PEM. The efficiency of H2 production depends on the different components of the PEM fuel cells, which are bipolar plates, current collector, and membrane electrode assembly. The efficiency also changes with the nature and type of the fuel, fuel/oxygen ratio, pressure, temperature, humidity, cell potential, and interfacial electronic level interaction between the redox levels of electrolyte and band gap edges of the semiconductor membranes. Diverse operating conditions such as concentration of methanol, cell temperature, catalyst loading, membrane thickness, and cell voltage that affect the performance are critically addressed. Comparison of various methanol electrolyzer systems are performed to validate the significance of methanol economy to match the future sustainable energy demands.

Modeling, Development, and Testing of a 2 MW Polymeric Electrolyte Membrane Fuel Cell Plant Fueled With Hydrogen From a Chlor-Alkali Industry

2019 ◽  
Vol 16 (4) ◽  
Author(s):  
Stefano Campanari ◽  
Giulio Guandalini ◽  
Jorg Coolegem ◽  
Jan ten Have ◽  
Patrick Hayes ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Pem Fuel Cell ◽  
Operating Conditions ◽  
Experimental Results ◽  
Complete Analysis ◽  
Membrane Electrode ◽  
Polymeric Electrolyte ◽  
Electrolyte Membrane ◽  
The Impact

The chlor-alkali industry produces significant amounts of hydrogen as by-product which can potentially feed a polymeric electrolyte membrane (PEM) fuel cell (FC) unit, whose electricity and heat production can cover part of the chemical plant consumptions yielding remarkable energy and emission savings. This work presents the modeling, development, and experimental results of a large-scale (2 MW) PEM fuel cell power plant installed at the premises of a chlor-alkali industry. It is first discussed an overview of project’s membrane-electrode assembly and fuel cell development for long life stationary applications, focusing on the design-for-manufacture process and related high-volume manufacturing routes. The work then discusses the modeling of the power plant, including a specific lumped model predicting FC stack behavior as a function of inlet stream conditions and power set point, according to regressed polarization curves. Cells’ performance decay versus lifetime reflects long-term stack test data, aiming to evidence the impact on overall energy balances and efficiency of the progression of lifetime. Balance of plant is modeled to simulate auxiliary consumptions, pressure drops, and components’ operating conditions. The model allows studying different operational strategies that maintain the power production during lifetime, minimizing efficiency losses, as well as to investigate the optimized operating setpoint of the plant at full load and during part-load operation. The last section of the paper discusses the experimental results, through a complete analysis of the plant performance after startup, including energy and mass balances and allowing to validate the model. Cumulated indicators over the first two years of operations regarding energy production, hydrogen consumption, and efficiency are also discussed.

Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Experimental Studies ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Coefficient Of Determination ◽  
Membrane Electrode ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell (DMFC) converts liquid fuel into electricity to power devices, while operating at relatively low temperatures and producing virtually no greenhouse gases. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (NN) represent a marked improvement in prediction capabilities. In this work, an artificial NN is employed to predict the performance of a DMFC under various operating conditions. Input variables for the analysis consist of methanol concentration, temperature, current density, number of cells, and anode flow rate. The addition of the two latter variables allows for a more distinctive model when compared to prior NN models. The key performance indicator of our NN model is cell voltage, which is an average voltage across the stack and ranges from 0 to 0.8 V. Experimental studies were conducted using DMFC stacks with membrane electrode assemblies consisting of an additional unique liquid barrier layer to minimize water loss to atmosphere. To determine the best fit to the experimental data, the model is trained using two second-order training algorithms: OWO-Newton and Levenberg–Marquardt (LM). The topology of OWO-Newton algorithm is slightly different from that of LM algorithm by employing bypass weights. The application of NN shows rapid construction of a predictive model of cell voltage for varying operating conditions with an accuracy on the order of 10−4, which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998.

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