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Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm

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  • Aminmahalati, Alireza
  • Fazlali, Alireza
  • Safikhani, Hamed

Abstract

In this study, the combustion chamber of a CO steam boiler is multi-objectively optimized (MOO) using computational fluid dynamics (CFD) and non-dominated sorted genetic algorithm II (NSGA II) algorithm. Multi-objective optimization with two input parameters and two objectives are followed. Input parameters are the two inlet air streams into the chamber called primary and secondary air streams. The optimization aims to maximize the outlet gas’s temperature and minimize the chamber’s wall’s temperature. Therefore, these two parameters are considered as the objectives of the optimization. To obtain an adequate number of nodes in the discretized system, a step-wise increase of the mesh is followed and finally, 6.1 million elements are selected. Due to the steam boiler complexity, some simplifications are assumed while it is assured that no remarkable error is introduced. Computations reveal that ignoring radiation at the beginning of the combustion chamber can cause an error as significant as 400 °C. In contrast, for the end of the combustion chamber, where optimization is performed over, an error up to 7.4 °C emerges. NSGA II algorithm is used for multi-objective optimization and the Pareto diagram is extracted and reported. Ultimately, the effect of different fluid intensity of primary and secondary air streams on different parameters like outlet temperature, gas composition in the outlet and the chamber’s wall temperature is presented.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221001080
    DOI: 10.1016/j.energy.2021.119859
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    Cited by:

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    2. Tang, Zhenhao & Sui, Mengxuan & Wang, Xu & Xue, Wenyuan & Yang, Yuan & Wang, Zhi & Ouyang, Tinghui, 2024. "Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction," Energy, Elsevier, vol. 299(C).
    3. Song, Dongran & Xu, Shanmin & Huang, Lingxiang & Xia, E. & Huang, Chaoneng & Yang, Jian & Hu, Yang & Fang, Fang, 2022. "Multi-site and multi-objective optimization for wind turbines based on the design of virtual representative wind farm," Energy, Elsevier, vol. 252(C).
    4. Zhang, Zhenyu & Cheng, Xiaoqing & Xing, Zongyi & Gui, Xingdong, 2023. "Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    5. Chen, Shuhang & Liu, Dongli & Li, Sizhuo & Gan, Zhihua & Qiu, Min, 2022. "Multi-objective thermo-economic optimization of Collins cycle," Energy, Elsevier, vol. 239(PD).
    6. Zhu, Yukun & Yu, Cong & Fan, Wei & Yu, Haiquan & Jin, Wei & Chen, Shuo & Liu, Xia, 2023. "A novel NOx emission prediction model for multimodal operational utility boilers considering local features and prior knowledge," Energy, Elsevier, vol. 280(C).
    7. Xu, Wentao & Huang, Yaji & Song, Siheng & Chen, Yuzhu & Cao, Gehan & Yu, Mengzhu & Chen, Bo & Zhang, Rongchu & Liu, Yuqing & Zou, Yiran, 2023. "A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle," Energy, Elsevier, vol. 263(PE).
    8. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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