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Advanced design optimization of combustion equipment for biomass combustion

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  • Smith, Joseph D.
  • Sreedharan, Vikram
  • Landon, Mark
  • Smith, Zachary P.

Abstract

of engineered combustion equipment normally involves laborious “build and try” designs to identify the best possible configuration. The number of design iterations can be reduced with engineering experience of what might work. The expensive cut-and-try approach can be improved using computational aided engineering tools coupled with optimization techniques to find the optimal design. For example, the “best” air duct configuration with the lowest pressure loss and smallest fan size for an air-fed biomass gasifier may take several weeks using the standard computational fluid dynamics (CFD) “cut and try” approach. Alternatively, coupling an efficient design optimization algorithm with an existing CFD model can reduce the time to find the best design by more than 50% and can allow the engineer to examine more design options than possible using the “cut-and-try” approach. Combining an efficient optimization algorithm with an existing CFD model of a biomass gasifier to find the “optimal” design is the focus of this work. Shape optimization has been performed by combining the optimization tool Sculptor® with the commercial CFD code STARCCM+. This work illustrates how the “linked” approach is used to examine design factors to optimize an entrained flow biomass gasifier to improve overall system performance in a methodical comprehensive fashion.

Suggested Citation

  • Smith, Joseph D. & Sreedharan, Vikram & Landon, Mark & Smith, Zachary P., 2020. "Advanced design optimization of combustion equipment for biomass combustion," Renewable Energy, Elsevier, vol. 145(C), pages 1597-1607.
  • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1597-1607
    DOI: 10.1016/j.renene.2019.07.074
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    Citations

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    Cited by:

    1. Wang, Linzheng & Zhang, Ruizhi & Deng, Ruiqu & Liu, Zeqing & Luo, Yonghao, 2023. "Comprehensive parametric study of fixed-bed co-gasification process through Multiple Thermally Thick Particle (MTTP) model," Applied Energy, Elsevier, vol. 348(C).
    2. Long Zhang & Shanshan Zhang & Hua Zhou & Zhuyin Ren & Hongchuan Wang & Xiuxun Wang, 2022. "Efficient Combustion of Low Calorific Industrial Gases: Opportunities and Challenges," Energies, MDPI, vol. 15(23), pages 1-14, December.
    3. Feldmeier, Sabine & Schwarz, Markus & Wopienka, Elisabeth & Pfeifer, Christoph, 2021. "Categorization of small-scale biomass combustion appliances by characteristic numbers," Renewable Energy, Elsevier, vol. 163(C), pages 2128-2136.
    4. Hosseinzadeh, Saman & Fattahi, Abolfazl & Sadeghi, Sadegh & Rahmani, Ebrahim & Bidabadi, Mehdi & Zarei, Fatemeh & Xu, Fei, 2020. "Mathematical analysis of steady-state non-premixed multi-zone combustion of porous biomass particles under counter-flow configuration," Renewable Energy, Elsevier, vol. 159(C), pages 705-725.
    5. Aminmahalati, Alireza & Fazlali, Alireza & Safikhani, Hamed, 2021. "Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm," Energy, Elsevier, vol. 221(C).

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