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Experimental investigation on the performance of a boiler system with flue gas dehumidification and combustion air humidification

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  • Liao, Weicheng
  • Zhang, Xiaoyue
  • Li, Zhen

Abstract

The temperature of the return water in heating networks is a key parameter for heat recovery from flue gas. At high the return water temperature, the conventional systems for boiler flue gas heat recovery do not perform well. This paper presents a more effective total heat recovery system by dehumidifying flue gas and humidifying combustion air. An experimental bench for flue gas waste heat recovery based on the combination of combustion air humidification and solution dehumidification was built to verify the feasibility of using potassium a formate aqueous solution (the concentration of the second dehumidifier was measured at 66%) to recover flue gas waste heat. The experimental results demonstrate that when the return water temperature of the heating network is as high as 52.5 °C, the boiler thermal efficiency increases by 9.1%. When the return water temperature of the heating network is 42–57 °C, the final relative humidity of the exhaust flue is 50–70% and the white plumes are remarkably eliminated. The equivalent thermal efficiency increases by >3% after the cooling tower is turned on. Compared with the existing waste heat recovery system, the experimental system developed in this study achieves a better waste heat recovery effect under the working conditions of a higher return water temperature of the heating network. Thus, this system holds promise for future applications as an energy-efficient, cost-effective, and environmentally-friendly system for waste heat recovery from district heating systems.

Suggested Citation

  • Liao, Weicheng & Zhang, Xiaoyue & Li, Zhen, 2022. "Experimental investigation on the performance of a boiler system with flue gas dehumidification and combustion air humidification," Applied Energy, Elsevier, vol. 323(C).
  • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009254
    DOI: 10.1016/j.apenergy.2022.119623
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    References listed on IDEAS

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    1. Homod, Raad Z. & Munahi, Basil Sh. & Mohammed, Hayder Ibrahim & Albadr, Musatafa Abbas Abbood & Abderrahmane, AISSA & Mahdi, Jasim M. & Ben Hamida, Mohamed Bechir & Alhasnawi, Bilal Naji & Albahri, A., 2024. "Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings," Applied Energy, Elsevier, vol. 356(C).

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