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

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

Research output: Contribution to journalArticle

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)463-474
Number of pages12
JournalApplied Energy
StatePublished - Aug 15 2019


  • Dual-layer granular filter
  • Dust holding capacity
  • Filtration efficiency
  • Genetic algorithm
  • Hot gas clean-up
  • Multi-objective optimization

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