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The Effect of Pore-Former Morphology on the Electrochemical Performance of Solid Oxide Fuel Cells under Combined Fuel Cell and Electrolysis Modes This paper discusses a critical aspect of fabricating high temperature fuel cell/electrolysis cells - the amount and morphology of the pore former added to the electrodes prior to sintering that is used to create porosity to minimize both activation and concentration polarization. Fine spherical polymethyl methacrylate (PMMA) proved superior to angular somewhat coarser graphite. The fine pore structure decreased activation polarization by increasing the triple phase boundary length but still provided sufficient porosity for unrestricted gas flow. Most importantly, reversibility experiments (alternating between fuel cell and electrolysis modes) showed no degradation in performance for over 400 h.T06-P04 University of Alberta Publication 2018-04-24 Miguel A Laguna-Bercero, A Laguna-Bercero, Miguel,
Hanifi, A. ,
" Lucile Manard
" , Navjot K Sandhu, Neil E Anderson, Thomas Etsell,
" Partha Sarkar
" T06-P04 High Performance Tubular Solid Oxide Fuel Cell based on Ba0.5Sr0.5Ce0.6Zr0.2Gd0.1Y0.1O3-d Proton Conductor Electrolyte Proton conducting electrolytes vs. oxygen ion conducting electrolytes have a major advantage in high temperature fuel cell/electrolysis cells - the fuel is not diluted as the cell is operated since water or CO2 is present at the air side rather than the fuel side. This novel composition was used to fabricate a tubular cell by a combination of slip casting and dip coating. Contrary to virtually all proton conductors, it appears chemically inert to both H2O vapour and CO2 as well as the other cell components. Correspondingly, it gave outstanding electrochemical performance producing a power output of 1 W/cm2 at 850C. This is among the highest output ever reported for a tubular cell with either a proton or oxygen ion conducting electrolyte. Electrochemical impedance spectroscopy was used in an effort to separate the various polarization losses.T06-P04, T02-C01 University of Alberta Publication 2018-04-24 Taghi Amiri,
" Kalpana Singh
" ,
Hanifi, A. , Thomas Etsell, Jingli Luo,
" Venkataraman Thangadurai
" ,
" Partha Sarkar
" T06-P04, T02-C01 Development of a Novel Proton Conducting Fuel Cell based on a Ni-YSZ Support One of the chief disadvantages of proton conducting fuel cells (and electrolytic cells) is a typical problem when ceramic materials are involved - poor mechanical properties. Robust oxygen ion conducting tubular cells have been fabricated with the Ni-yttria stabilized zirconia (YSZ) composite as the cell support (serves also as one of the electrodes). Not only does YSZ provide reasonable ionic conductivity and excellent chemical inertness but it is one of the best ceramic materials with respect to fracture toughness. This success has been capitalized on by using Ni-YSZ as the support for a tubular proton conducting cell. Reasonable power outputs from 600-700C were obtained. This could greatly increase the longevity and decrease fabrication costs of proton conducting cells. T06-P04 University of Alberta Publication 2018-04-24 Hanifi, A. , Navjot K Sandhu, Thomas Etsell,
" Partha Sarkar
" T06-P04 Tetragonal Zirconia as Next Generation Support for Dry Reforming Solid Oxide Fuel Cell The usual structural support for ceramic fuel cells is nickel plus fully stabilized cubic zirconia containing 8 mol % Y2O3. This is the electrolyte composition as well. It has very good mechanical properties (at least relative to most ceramics). However, partially stabilized two-phase zirconia (tetragonal + cubic) containing 3 mol % Y2O3 has excellent mechanical properties due to monoclinic-to-tetragonal transformation toughening. Also, when mixed with nickel (the support also serves as one of the electrodes), it appears to slow down nickel agglomeration with an attendant loss of electronic conductivity. However, it has lower ionic conductivity that impacts the amount of triple phase interface (gas, electrode, electrolyte) available for electrochemical reaction. Preliminary results under syngas with the new support composition resulted in comparable, if not better, power outputT06-P04 University of Alberta Activity 2017-10-24 Taghi Amiri,
Hanifi, A. , Thomas Etsell, Jingli Luo,
" Partha Sarkar
" T06-P04 Development of Proton Conducting Fuel Cells using Nickel Metal Support The use of nickel, an excellent electronic conductor and YSZ, an good ionic conductor to the cell support greatly increases the current collection capability and mechanical properties of proton conducting fuel cells. These results will have important implications in using such cells in electrolysis mode to generate hydrogen or especially in reversible mode for load levelling of renewable wind or solar energy.T06-P04 University of Alberta Publication 2019-06-25 Sajad Vafaeenezhad, Navjot Kaur Sandhu,
Hanifi, A. , Thomas Etsell,
" Partha Sarka
" T06-P04 Ni-YSZ a New Support for Proton Conducting Fuel Cells The addition of only a small amount of YSZ (10 wt%) to the Ni support reduces polarization resistance and prevents severe Ni grain growth thereby providing a higher density of electrochemically active sites at the support /anode functional layer interface and more uniform distribution of fuel gas to the active sites. Perhaps most importantly it improves the mechanical properties of notoriously fragile proton conducting cells, a particularly critical consideration when developing reversible fuel cell/electrolysis cells that are particularly prone to cracking.T06-P04 University of Alberta Publication 2019-06-08 Sajad Vafaeenezhad, Navjot Kaur Sandhu,
Hanifi, A. , Thomas Etsell,
" Partha Sarkar
" T06-P04 Microstructure and Long Term Stability of Ni-YSZ Anode Supported Fuel Cells: A Review A comprehensive review article focused on the importance that stability measurements be included in research papers as they are ultimately much more important than the initial electrolytic or fuel cell behaviour. Degradation issues is the main technical reason limiting widespread commercialization of SOEC/SOFCs.T06-P04 University of Alberta Publication 2021-05-12 Sajad Vafaeenezhad,
Hanifi, A. , Miguel A Laguna-Bercero, Thomas Etsell,
" Partha Sarkar
" T06-P04 Tailoring the solid oxide fuel cell anode support composition and microstructure for low-temperature applications T06-P04 University of Alberta Publication 2023-03-01 T06-P04 Stability of infiltrated cathodes using Pr2NiO4+delta precursor for low-temperature fuel cell applications T06-P04 University of Alberta Publication 2022-09-01 Sajad Vafaeenezhad, Miguel A Morales-Zapata,
Hanifi, A. , Miguel A Laguna-Bercero,
" Ángel Larrea
" , Partha Sarkar, Thomas Etsell
T06-P04 Performance and Stability of Infiltrated Praseodymium Nickelate Cathodes for Low-Temperature Fuel Cell Applications T06-P04 University of Alberta Publication 2022-01-01 Sajad Vafaeenezhad, Miguel A Morales-Zapata,
Hanifi, A. , Miguel A Laguna-Bercero, Á ngel Larrea, Partha Sarkar, Thomas Etsell
T06-P04 Transient Modeling of a Solid Oxide Fuel Cell using an Efficient Deep Learning HY-CNN-NARX Paradigm Control and monitoring systems are crucial for ensuring optimal performance, efficiency, and longevity of Solid Oxide Fuel Cells (SOFC). Developing a model to accurately capture the transient behavior of the SOFC under dynamic operation is essential for these applications. As electrochemical, mass-transfer, and thermal equations are involved in SOFCs’ dynamic, physics-based approaches to capture all the complexity of SOFC systems are often too computationally expensive for real-time implementation. In this paper, an innovative multi-input neural network is developed to build an accurate model of SOFC that is computationally efficient. The proposed algorithm uses experimental data and is a modified nonlinear autoregressive exogenous (NARX) network. An optimal sequence of the most recent observations (output voltages) that characterize the SOFC dynamics is passed through stacked 1D convolutional layers (CNN) to extract latent spatial/temporal information. Then the output is concatenated with current and past exogenous inputs. The fusion of learned features, representing the internal dynamics and the effect of exogenous inputs, is then fed to a fully connected network for one step ahead performance prediction. This hybrid structure, called HY-CNN-NARX, is capable of identifying the transient dynamics of the SOFCs by unrolling information from historical outputs and inputs within a feedforward framework. To evaluate the model performance, lab-scale tubular SOFCs are experimentally tested at 650–750 ◦C under various dynamic operations and initial conditions, and the transient and steady-state responses of the SOFC are collected. Once the model is validated using a wide operating range of data, the generalization and prediction performance of the proposed network is compared with a conventional NARX and a recurrent stacked Long short-term memory (LSTM) model. The comparative results demonstrate that the proposed HY-CNN-NARX model outperforms the NARX model on unseen data with an accuracy improvement of 59.2% for root mean square error and 53.7% for mean absolute percentage error. In addition, the prediction performance of the HY-CNN-NARX is comparable to its recurrent counterpart, the LSTM model, but with a 39.3% faster execution time.T06-A02 University of Alberta Publication 2024-04-14 T06-A02 Developing a Time-Efficient Model for Solid Oxide Fuel Cells Using Self-Supervised Convolutional Autoencoder and Stateful LSTM Network The development of an efficient dynamic model for solid oxide fuel cell (SOFC) systems is essential for control strategies, process optimization, and fault diagnostics - key steps toward maximizing power generation and extending their lifetime. Long-Short-Term-Memory (LSTM), a potent type of
recurrent neural network, has proven to be adept at modeling intricate physical dynamic systems using extensive input-output data. They are particularly well-suited for capturing longrange
dependencies and temporal patterns. Nevertheless, due to memory and computing constraints in practice, training an LSTM network to effectively learn long-term dependency is challenging, which stems from the sequential processing nature of LSTMs. In this study, a self-supervised convolutional
autoencoder (AE) is developed to learn a concise yet informative temporal representation of historical input data. These compressed hidden sequences are then input into an LSTM model,
which is trained using a truncated backpropagation through time (TBPTT) algorithm. Once trained, an identical stateful LSTM model is reconstructed using the learned parameters, enabling the LSTM to predict the voltage using only the most recent past input data, rather than a sequence of data over
which the model was trained. The efficacy of the proposed framework is validated utilizing diverse experimental data collected from a lab-scale SOFC. The results indicate while the model properly identifies the underlying dynamics of the SOFC, it significantly reduces the training and runtime prediction computation costs of a traditional LSTM model by 38% and 42% respectively. This enhancement holds promise for improving real-time control and diagnostics of SOFC systems,
ultimately contributing to their improved performance and reliability.T06-A02 University of Alberta Publication 2024-07-08 T06-A02 Thermodynamic and Economic Analysis of a Novel Concept for Methane Pyrolysis in Molten Salt Combined with Heliostat Solar Field Methane pyrolysis has emerged as a promising low-carbon hydrogen production method, serving as a bridge from conventional fossil fuels to renewable energies (REs). A recent technique for sustainable hydrogen production involves the process of methane pyrolysis in molten salt. To better understand the potential of sustainable turquoise hydrogen production, this paper presents a technoeconomic assessment of methane pyrolysis in molten salt combined with a high-temperature heliostat solar field. In addition, a high-temperature thermal energy storage (HTES) unit is utilized to ensure uninterrupted hydrogen production during periods of solar unavailability, thereby contributing to the economic viability of the overall system. The proposed system can produce 9 tons of H 2 /day and 27 tons of solid carbon/day with a round-trip efficiency of 49.8% and a levelized cost of hydrogen (LCOH) of 1.93 $/kg. To further decarbonize the process, the feasibility of utilizing renewable energies as the source of electricity for the HTES was analyzed, yielding a synergistic achievement of clean and economic hydrogen production. This configuration resulted in a LCOH of 1.25 $/kg, after including the United States (US) clean hydrogen production tax incentives. The proposed system shows the potential to achieve the US Department of Energy target of 1 $/kg of hydrogen with a 20% reduction in the cost of RE infrastructure.T06-A02 University of Alberta Publication 2024-06-01 T06-A02 Control-oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling using a Novel Deep Learning Approach T06-A02 University of Alberta Publication 2024-10-27 T06-A02 Modeling and microstructural study of anode-supported solid oxide fuel cells: Experimental and thermodynamic analyses Developing novel solid oxide fuel cells (SOFCs) with high stability running at low temperatures is an important objective in SOFC science. In the current paper, a comprehensive physics-based microstructure modeling using scanning electron microscope (SEM) image analysis was performed on several anode-support SOFCs operating at low temperatures with high stability. To bridge the gap in the literature regarding an accurate and realistic modeling, a new model was developed based on the variable fuel and air utilization factors and updated microstructure values (e.g., tortuosity, porosity, pore size, grain size). The model accuracy was verified by a thorough point-to-point validation for eight different cells with the configuration of Ni-YSZ (anode), YSZ (electrolyte), and GDC/PNO (cathode). Different temperatures, hydrogen, and air mass flow rates were used, for which an average error of less than 3% in the I-V curves was achieved. The microstructure of the cells, including cathode thickness (15–26 μm), anode-support thickness (350–460 μm), porosity (39 and 43%), grain size (1.1–1.4 μm), and pore radius (0.9–1.1 μm) were varied. Moreover, the effects of the critical operational and design parameters on the overpotential losses and cell performance were studied. The results show that a hydrogen flow rate of 43 sccm was ideal when the cell operated at 0.9 A/cm2 and 700 °C. Moreover, an average anode-support pore radius of 1.75 μm resulted in the best cell performance. It was also concluded that the electrolyte thickness has a higher effect on the cell performance compared to the cathode thicknessT06-A02 University of Alberta Publication 2023-09-13 T06-A02 Design, thermodynamic, and economic analyses of a green hydrogen storage concept based on solid oxide electrolyzer/fuel cells and heliostat solar field Green hydrogen production is facing challenges in balancing economic feasibility with sustainability. Employing efficient hydrogen production designs and benefiting from the potential of hydrogen storage provide two promising strategies to mitigate the economic constraints associated with green hydrogen production. This paper proposes a novel hybrid design for green hydrogen production/utilization based on efficient high-temperature units, including heliostat solar field, solid oxide electrolyzer cell (SOEC), and solid oxide fuel cell (SOFC). The proposed system is comprehensively investigated from thermodynamic and economic perspectives, along with conducting a case study based on hourly electricity prices and actual solar data. The system demonstrates a hydrogen production rate of 7.76 ton/day using the SOEC and a power generation of 54.3 MWh in the SOFC for peak demand shaving, yielding an overall round trip efficiency of 74.2%. The case study results indicate that the economic feasibility is significantly compromised if all the produced hydrogen is sold at prices below 2.75 $/kg; while, implementation of hydrogen storage for peak shaving can yield a promising payback period of less than 2 years. The hydrogen and hourly electricity prices, along with the duration of peak times, are the other critical factors that affect economic viability.T06-A02 University of Alberta Publication 2023-07-08 T06-A02 SOFC applications as combined Heat and Power Units for Canada Dr. Amir Hanifi, who holds the position of a Senior Science and Technology Advisor at the Canadian government, has consistently highlighted the pivotal role of SOFC and SOEC in shaping Canada's hydrogen economy throughout the project's duration. He has made it a point to enlighten senior officials in the Federal Government about the potential of SOFC to be used as combined heat and power for residential use, and the importance of SOFC and SOEC in power-to-gas-to-power (P2G2P) cycles. His efforts have significantly elevated the level of awareness and understanding of this key technology within the federal government.T06-A02 University of Alberta Activity 2024-03-01 T06-A02 Canadian Hydrogen Convention T06-A02 University of Alberta Activity 2023-04-24 T06-A02 2024 Canadian Hydrogen Convention Attended 2024 Canadian Hydrogen Convention and discussed collaboration with potential partners in Strathcona County to test SOFC for combined heat and power (CHP) application in their planned facility to create a hydrogen fueled CHP testbed using their recent ERA funding.T06-A02 University of Alberta Activity 2024-04-23 T06-A02 SOFC Database Three tubular cells with different design parameters are fabricated, and each is tested under 18 different operating conditions. Moreover, a planar cell is tested under 10 operating conditions. Tubular and planar cells are substantially different in cell parameters and tested under a wide range of operating conditions. At each operating condition, fuel cell polarization curves are collected. A dataset of 23,820 samples from these cells, that could be useful for data-driven modeling is obtained and available as open source to the communityT06-A02 University of Alberta Publication 2024-03-01 T06-A02 Temporal Dilated Convolution and Nonlinear Autoregressive Network for Predicting Solid Oxide Fuel Cell Performance T06-A02 University of Alberta Publication 2024-10-01 T06-A02 Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells T06-A02 University of Alberta Publication 2025-01-01 T06-A02
