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Multi-objective optimization of a dual-layer granular filter for hot gas clean-up by using genetic algorithm

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  • Wang, Fei-Long
  • He, Ya-Ling
  • Tang, Song-Zhen
  • Kulacki, Francis A.
  • Tao, Yu-Bing

Abstract

Granular filters are one of the most promising methods for hot gas clean-up in coal-based power generation systems. In order to further improve the filtration performance, a dual-layer granular filter is proposed, and multi-objective optimization via genetic algorithm is applied to optimize the geometric parameters. The optimization result is a Pareto front which includes a set of optimal solutions. Users can select each solution based on trade-offs between filtration efficiency, pressure drop, dust holding capacity and project limitations. An optimal case with relatively high efficiency, high deposition uniformity and low pressure drop is selected from the Pareto front. Numerical simulations are performed, and comparison of filtration performance between the optimal case and a single layer filter are conducted, to verify the superiority of the optimum structure. The optimum dual-layer granular filter has higher filtration efficiency than the single layer filter but with the expense of a small amount of pressure drop. The dual-layer granular filter also has higher deposition uniformity and higher dust holding capacity. Cleaning intervals for the dual-layer filter can be increased approximately 0.4 h, which gives the dual-layer filter better economic performance. The larger granular size in the first layer of dual-layer granular filter can let particles pass-through and deposit more uniformly than in the single layer filter, thus improving the dust holding capacity. The smaller granular size in the second layer also gives it better performance when filtering small particles.

Suggested Citation

  • Wang, Fei-Long & He, Ya-Ling & Tang, Song-Zhen & Kulacki, Francis A. & Tao, Yu-Bing, 2019. "Multi-objective optimization of a dual-layer granular filter for hot gas clean-up by using genetic algorithm," Applied Energy, Elsevier, vol. 248(C), pages 463-474.
    • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:463-474
    DOI: 10.1016/j.apenergy.2019.04.152
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    References listed on IDEAS

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    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Wang, Kun & He, Ya-Ling & Xue, Xiao-Dai & Du, Bao-Cun, 2017. "Multi-objective optimization of the aiming strategy for the solar power tower with a cavity receiver by using the non-dominated sorting genetic algorithm," Applied Energy, Elsevier, vol. 205(C), pages 399-416.
    3. Li, Ming-Jia & He, Ya-Ling & Tao, Wen-Quan, 2017. "Modeling a hybrid methodology for evaluating and forecasting regional energy efficiency in China," Applied Energy, Elsevier, vol. 185(P2), pages 1769-1777.
    4. Ganjehkaviri, A. & Mohd Jaafar, M.N. & Hosseini, S.E. & Barzegaravval, H., 2016. "On the optimization of energy systems: Results utilization in the design process," Applied Energy, Elsevier, vol. 178(C), pages 587-599.
    5. Sanaye, Sepehr & Hajabdollahi, Hassan, 2010. "Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm," Applied Energy, Elsevier, vol. 87(6), pages 1893-1902, June.
    6. Li, Ming-Jia & Song, Chen-Xi & Tao, Wen-Quan, 2016. "A hybrid model for explaining the short-term dynamics of energy efficiency of China’s thermal power plants," Applied Energy, Elsevier, vol. 169(C), pages 738-747.
    7. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
    8. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    9. Tang, Song-Zhen & Wang, Fei-Long & He, Ya-Ling & Yu, Yang & Tong, Zi-Xiang, 2019. "Parametric optimization of H-type finned tube with longitudinal vortex generators by response surface model and genetic algorithm," Applied Energy, Elsevier, vol. 239(C), pages 908-918.
    10. Wang, Kun & Li, Ming-Jia & Guo, Jia-Qi & Li, Peiwen & Liu, Zhan-Bin, 2018. "A systematic comparison of different S-CO2 Brayton cycle layouts based on multi-objective optimization for applications in solar power tower plants," Applied Energy, Elsevier, vol. 212(C), pages 109-121.
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