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<p>The thermogravimetric analyzer and flue gas analyzer were utilized to investigate the co-combustion behaviors and gas pollutant emissions of coal slime (CS) and moso bamboo (MB) mixtures in O<sub>2</sub>/CO<sub>2</sub> atmospheres. Principal component analysis was employed to ascertain the contribution rate and identify the primary reactions of CS and MB combustion. Four kinetic methods were applied to calculate the apparent activation energy (<i>E</i><sub>α</sub>) and the most probabilistic mechanism function. The data exhibited that higher oxygen concentrations could promote combustion, and adding MB to CS further improved combustion performance. Co-combustion of CS and MB effectively inhibited total SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions; specifically, the NO<sub><i>x</i></sub> conversion rate was lower than that observed in air, whereas the SO<sub>2</sub> conversion rate exceeded that found in air. The contribution rate of two major components to the total variance was 99.35%. A blending ratio of 50% MB is recommended owing to the stronger synergistic promotion and lower <i>E</i><sub>α</sub>. Furthermore, the most probable mechanism function for CS was <i>g</i>(<i>α</i>) = 1 − (1 − <i>α</i>)<sup>1/4</sup>; the mechanism was reaction order. The <i>E</i><sub>α</sub> prediction model of CS and MB co-combustion under oxy-fuel conditions was established using artificial neural networks.</p>

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Co-combustion characteristics and kinetic analyses of coal slime and moso bamboo blends under oxy-fuel conditions

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Xiufen Ma,
  • Haifeng Ning,
  • Zhenjuan Zang,
  • Peiyong Ma,
  • Xu Hu,
  • Xianjun Xing

摘要

The thermogravimetric analyzer and flue gas analyzer were utilized to investigate the co-combustion behaviors and gas pollutant emissions of coal slime (CS) and moso bamboo (MB) mixtures in O2/CO2 atmospheres. Principal component analysis was employed to ascertain the contribution rate and identify the primary reactions of CS and MB combustion. Four kinetic methods were applied to calculate the apparent activation energy (Eα) and the most probabilistic mechanism function. The data exhibited that higher oxygen concentrations could promote combustion, and adding MB to CS further improved combustion performance. Co-combustion of CS and MB effectively inhibited total SO2 and NOx emissions; specifically, the NOx conversion rate was lower than that observed in air, whereas the SO2 conversion rate exceeded that found in air. The contribution rate of two major components to the total variance was 99.35%. A blending ratio of 50% MB is recommended owing to the stronger synergistic promotion and lower Eα. Furthermore, the most probable mechanism function for CS was g(α) = 1 − (1 − α)1/4; the mechanism was reaction order. The Eα prediction model of CS and MB co-combustion under oxy-fuel conditions was established using artificial neural networks.