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Performance of flue gas quench and its influence on biomass fueled CHP

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  • Li, Hailong
  • Wang, Bin
  • Yan, Jinying
  • Salman, Chaudhary Awais
  • Thorin, Eva
  • Schwede, Sebastian

Abstract

For biomass/waste fueled power plants, stricter regulations require a further reduction of the negative impacts on the environment caused by the release of pollutants and withdrawal of fresh water externally. Flue gas quench (FGQ) is playing an important role in biomass or waste fueled combined heat and power (CHP) plants, as it can link the flue gas (FG) cleaning, energy recovery and wastewater treatment. Enhancing water evaporation can benefit the concentrating of pollutant in the quench water; however, when FG condenser (FGC) is not in use, it results in a large consumption of fresh water. In order to deeply understand the operation of FGQ, a mathematic model was developed and validated against the measurements. Based on simulation results key parameters affecting FGQ have been identified, such as the flow rate and temperature of recycling water and the moisture content of FG. A guideline about how to reduce the discharge of wastewater to the external and the withdrawal of external water can be proposed. The mathematic model was also implemented into an ASPEN Plus model about a CHP plant to assess the impacts of FGQ on CHP. Results show that when the FGC was running, increasing the flow rate and decreasing the temperature of recycling water can result in a lower total energy efficiency.

Suggested Citation

  • Li, Hailong & Wang, Bin & Yan, Jinying & Salman, Chaudhary Awais & Thorin, Eva & Schwede, Sebastian, 2019. "Performance of flue gas quench and its influence on biomass fueled CHP," Energy, Elsevier, vol. 180(C), pages 934-945.
    • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:934-945
    DOI: 10.1016/j.energy.2019.05.078
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    1. Hui Huang & Xiaoli Yan & Shizhong Song & Yingying Du & Yanlei Guo, 2020. "An Economic and Technology Analysis of a New High-Efficiency Biomass Cogeneration System: A Case Study in DC County, China," Energies, MDPI, vol. 13(15), pages 1-21, August.
    2. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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