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Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants

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  • Nakamura, Kotaro
  • Muramatsu, Takehiko
  • Ogawa, Takashi
  • Nakagaki, Takao

Abstract

As variable renewable energy expands, natural gas-fired combined cycle (NGCC) power plants are expected to provide important functions for grid stabilization such as quick start-up, shutdown, and load following. However, transient operation of NGCC significantly increases the NO2 content of NOX in the exhaust gas and reduces de-NOx performance of selective catalytic reduction (SCR) with ammonia injection. The denitrification performance of SCR depends on transient mechanisms such as adsorption of ammonia in the catalyst and redox reactions on the catalyst surface. This study evaluates whether de-NOX performance can be maintained during expected operational fluctuations for grids with a high penetration of variable renewable energy. Simulations involving a modified de-NOX reaction scheme have been developed and validated for various NGCC exhaust gas compositions expected in both steady state and transient operation using a commercial, monolithic SCR catalyst. Results show that sudden output load changes cause a decrease in SCR catalyst performance due to changes in gas composition and temperature outpacing the adsorption/desorption of ammonia in the catalyst. It was found that adjusting the injection amount of ammonia several minutes prior to the output load change was effective in maintaining de-NOX performance.

Suggested Citation

  • Nakamura, Kotaro & Muramatsu, Takehiko & Ogawa, Takashi & Nakagaki, Takao, 2021. "Prediction of de-NOx performance using monolithic SCR catalyst under load following operation of natural gas-fired combined cycle power plants," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221006320
    DOI: 10.1016/j.energy.2021.120383
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    References listed on IDEAS

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    1. Shin‐ichi Inage, 2015. "The role of large‐scale energy storage under high shares of renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 4(1), pages 115-132, January.
    2. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
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    Cited by:

    1. Zhang, Xin & Chen, Zhichao & Hou, Jian & Liu, Zheng & Zeng, Lingyan & Li, Zhengqi, 2022. "Evaluation of wide-range coal combustion performance of a novel down-fired combustion technology based on gas–solid two-phase flow characteristics," Energy, Elsevier, vol. 248(C).
    2. Yan, Peiliang & Fan, Weijun & Zhang, Rongchun, 2023. "Predicting the NOx emissions of low heat value gas rich-quench-lean combustor via three integrated learning algorithms with Bayesian optimization," Energy, Elsevier, vol. 273(C).
    3. Ouyang, Tiancheng & Tan, Jiaqi & Wu, Wencong & Xie, Shutao & Li, Difan, 2022. "Energy, exergy and economic benefits deriving from LNG-fired power plant: Cold energy power generation combined with carbon dioxide capture," Renewable Energy, Elsevier, vol. 195(C), pages 214-229.

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