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All issues Volume 245 (2021) E3S Web Conf., 245 (2021) 03007 References
Open Access
Issue
E3S Web Conf.
Volume 245, 2021
2021 5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2021)
Article Number 03007
Number of page(s) 8
Section Chemical Performance Research and Chemical Industry Technology Research and Development
DOI https://doi.org/10.1051/e3sconf/202124503007
Published online 24 March 2021
  1. DONG Bin-qi, LI Chu-fu, LIU Chang-lei, et al. Power generation technology of coal gasification fuel cell with near zero CO2 emission and its challenges [J]. Coal science and technology, 2019, 47(07): 189-193. [Google Scholar]
  2. Han Minfang. Technological Progress and Industry Prospect of Solid Oxide Fuel Cell (SOFC) [J]. Democracy and Science, 2017 (05): 25-26. [Google Scholar]
  3. Singhal S C. Advances in solid oxide fuel cell technology[J]. Solid State Ionics, 2000, 135(1): 305-313. [Google Scholar]
  4. Radenahmad N, Azad A T, Saghir M, et al. A review on biomass derived syngas for SOFC based combined heat and power application[J]. Renewable & sustainable energy reviews, 2020, 119: 109560. [Google Scholar]
  5. Radenahmad N, Azad A T, Saghir M, et al. A review on biomass derived syngas for SOFC based combined heat and power application[J]. Renewable and Sustainable Energy Reviews, 2020, 119: 109560. [Google Scholar]
  6. Choudhury A, Chandra H, Arora A. Application of solid oxide fuel cell technology for power generation—A review[J]. Renewable and Sustainable Energy Reviews, 2013, 20: 430-442. [Google Scholar]
  7. Qu Z, Aravind P V, Verkooijen A, et al. Flow Distribution in the External Manifold of SOFC Stack. Asme International Conference on Energy Sustainability Collocated with the Heat Transfer, 2009. [Google Scholar]
  8. Wang J. Theory and practice of flow field designs for fuel cell scaling-up: A critical review[J]. Applied Energy, 2015, 157: 640-663. [Google Scholar]
  9. Chen C, Jung S, Yen S. Flow distribution in the manifold of PEM fuel cell stack[J]. Journal of power sources, 2007, 173(1): 249-263. [Google Scholar]
  10. Chen D, Zeng Q, Su S, et al. Geometric optimization of a 10-cell modular planar solid oxide fuel cell stack manifold[J]. Applied Energy, 2013, 112: 1100-1107. [Google Scholar]
  11. Zhao C, Yang J, Zhang T, et al. Numerical simulation of flow distribution for external manifold design in solid oxide fuel cell stack[J]. International Journal of Hydrogen Energy, 2017, 42(10): 7003-7013. [Google Scholar]
  12. Zhao C, Yang J, Zhang T, et al. Numerical modeling of manifold design and flow uniformity analysis of an external manifold solid oxide fuel cell stack[J]. International Journal of Hydrogen Energy, 2020, 45(28): 14440-14451. [Google Scholar]
  13. Li Yijin, Song Hongchao, Zhou Shuai, et al. Application of Artificial Intelligence in Computational Fluid Dynamics [J]. Aerospace power, 2018(01): 57-59. [Google Scholar]
  14. DU Mengxing, Yanwei W, Xiangzhi Z. Optimal Design of Centrifugal Pump Based on RBF Neural Network and Genetic Algorithm[J]. Journal of China Three Gorges University (Natural Sciences), 2020, 42(4): 88-93. [Google Scholar]
  15. Lei-Lei Y, Hao L U, Chun Y U, et al. Prediction of CFD temperature in pressure vessel by applying BP neural network[J]. Transactions of Materials and Heat Treatment, 2016, 37(12): 173-179. [Google Scholar]
  16. Jiang Wenzhi, Duan Shengqiu, Yang Changming, et al. Optimal Design of Centrifugal Pump Impeller Based on CFD Simulation and BP Neural Network [J]. Machine Tool & Hydraulics, 2016, 44(22): 67-70. [Google Scholar]
  17. Wenzhi J, Shengqiu D, Changming Y, et al. Optimal Design of Centrifugal Pump Impeller Based on CFD Simulations and BP Neural Network[J]. Machine Tool & Hydraulics, 2016, 44(22): 67-70. [Google Scholar]
  18. WEN Xin. Intelligent Fault Diagnosis Technology: MATLAB Application [M]: Beijing University of Aeronautics and Astronautics Press. 2015:30. [Google Scholar]
  19. WEN Xin. Application Design of MATLAB Neural Network [M]: Science Press, 2000:65. [Google Scholar]
  20. Xuan W, Qing-Yi S. Multi-factor optimization and design of centrifugal fan based on orthogonal experiment method[J]. Energy Engineering, 2020, (4): 13-18. [Google Scholar]
  21. Jiang Zhongzhong, Cai Zixing, Wang Yong. Hybrid Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems [J]. Journal of Software, 2010 (06): 1296-1307. [Google Scholar]
  22. Zeng Qice. Flow Field Simulation and Structure Optimization of Modular Short Reactor of Solid Oxide Fuel Cell [D]. Jiangsu University of Science and Technology, 2012:27. [Google Scholar]
  23. Shi Ying, Wang Xuezhi, Liu Jinying, et al. Thermal Characteristics Analysis of Hydrostatic and Hydrostatic Bearings Based on Fluent-BP Neural Network [J]. Lubrication and sealing, 2016, 41(01): 37-42. [Google Scholar]
  24. Guo Ying. Research on Multi-objective Optimization Design and Energy Conversion of Cyclonic Pump Based on Orthogonal Test [D]. Lanzhou University of Technology, 2020:41. [Google Scholar]

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