Thermodynamic analyses of a standalone diesel-fueled distributed power generation system based on solid oxide fuel cells

Pan, Zehua; Shen, Jian; Wang, Jingyi; Xu, Xinhai; Chan, Wei Ping; Liu, Siyu; Zhou, Yexin; Yan, Zhimiao; Jiao, Zhenjun; Lim, Tuti Mariana; Zhong, Zheng

doi:10.1016/j.apenergy.2021.118396

Cited by 27 publications

(3 citation statements)

References 67 publications

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“…Therefore, S/C was ultimately set to 3.5, which is relatively higher, to reduce carbon deposition in SOFC and the reformer. The feasibility of this S/C was demonstrated by previous research [1] .…”

Section: Working Condition Of the Reformermentioning

confidence: 80%

“…Some methods are being considered to improve the system efficiency, such as changing system configurations and optimizing operation methods. Zehua Pan et al [1] put forward a system design that removed stream from the syngas before it entered the cell. Although the liquid water would take away some heat, a higher system efficiency was achieved due to the higher concentration of H2 in the syngas.…”

Section: Introductionmentioning

confidence: 99%

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Configurations and Operation Methods of Diesel-Based Solid Oxide Fuel Cell System

Yang¹,

Liu²,

Han³

2023

ECS Trans.

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Solid oxide fuel cell (SOFC) can use a wide variety of carbon-based fuels. Diesel can act as a high-quantity fuel because of its high heat value and ease of transport. In this study, three different system configurations were proposed by a model in ASPEN PLUS V12 and fitted by data measured in the large size cell. The effects of key operation parameters, including reforming temperature, steam-to-carbon ratio (S/C) and recycling rate of the anode off-gas, were studied, which were characterized by the system efficiency and output power. As a result, the highest system efficiency was gotten with a water condenser and an anode off-gas recycling module in the system. The best operation method occurs when the thermal load of the diesel reformer is compensated by the heat released by anode off-gas combustion. This research will lay a theoretical foundation for the upcoming design of a physical diesel-based SOFC system.

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Section: Working Condition Of the Reformermentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Configurations and Operation Methods of Diesel-Based Solid Oxide Fuel Cell System

Yang¹,

Liu²,

Han³

2023

ECS Trans.

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“…The dataset was augmented through image flipping and rotation. The dataset contains microstructural images and corresponding macroscopic properties of nickel-yttria stabilized zirconia (Ni-YSZ) anodes for solid oxide fuel cells (SOFCs), known as green energy conversion devices (Yan et al, 2019b;Xu et al, 2021;Pan et al, 2022). In these numerical samples of Ni-YSZ composite anodes, the porosity was varied in the range of 0-20% and the proportion of Ni in the Ni-YSZ was varied in the range of 30-70%.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation and data mining towards microstructure and property relationship for composites

Guo

Liu

Pan

et al. 2023

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PurposeIn recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.Design/methodology/approachMicrostructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.FindingsThe comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.Originality/valueThis study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

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Influence of Structural Parameters of Catalyst Support on Hydrogen Production Performance of Diesel Reforming

Wang,

Qin

2024

Springer Proceedings in Physics

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Thermodynamic analyses of a standalone diesel-fueled distributed power generation system based on solid oxide fuel cells

Cited by 27 publications

References 67 publications

Configurations and Operation Methods of Diesel-Based Solid Oxide Fuel Cell System

Configurations and Operation Methods of Diesel-Based Solid Oxide Fuel Cell System

Data augmentation and data mining towards microstructure and property relationship for composites

Influence of Structural Parameters of Catalyst Support on Hydrogen Production Performance of Diesel Reforming

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