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<p>During the co-combustion process, different fuel types degrade at different rates and temperatures due to their complex chemical structures and elemental compositions. Predicting the co-combustion behavior of solid fuels is an effective method for the effective utilization. In this study, co-combustion experiments were performed on the raw woodchip (WC), torrefied woodchip at 523&#xa0;K (TWC), and anthracite coal (QN coal) blends at various ratios of 100%, 60%, 40%, and 0%. A multi-pseudo-distributed activation energy model was proposed to describe the combustion behaviors of pure biomass, coal, and a mixture of coal and biomass. The interaction between QN coal and WC/TWC was very complex, showing a different trend of the activation energy in the third stage of oxidation. The proposed artificial neural network (NN-3-30-30-2) with three layers of 30 neurons, 30 neurons, and logsig-logsig transfer functions can be used to evaluate the mass loss and derived the masses loss during the co-combustion process. The input parameters were optimized using particle swarm optimization (PSO) to maximize the mass loss in the co-combustion process. At a temperature and heating rate of 602&#xa0;K and 40&#xa0;K min<sup>−1</sup>, respectively, optimization of 60% of TWC using PSO resulted in a DTG<sub>max</sub> value of 84.868 (% min<sup>−1</sup>).</p>

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Co-combustion and blending optimization of anthracite coal and raw/torrefied woodchip using particle swarm optimization

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Viet Thieu Trinh,
  • Hyeong-Bin Moon,
  • Seung-Mo Kim,
  • Byoung-Hwa Lee,
  • Chung-Hwan Jeon

摘要

During the co-combustion process, different fuel types degrade at different rates and temperatures due to their complex chemical structures and elemental compositions. Predicting the co-combustion behavior of solid fuels is an effective method for the effective utilization. In this study, co-combustion experiments were performed on the raw woodchip (WC), torrefied woodchip at 523 K (TWC), and anthracite coal (QN coal) blends at various ratios of 100%, 60%, 40%, and 0%. A multi-pseudo-distributed activation energy model was proposed to describe the combustion behaviors of pure biomass, coal, and a mixture of coal and biomass. The interaction between QN coal and WC/TWC was very complex, showing a different trend of the activation energy in the third stage of oxidation. The proposed artificial neural network (NN-3-30-30-2) with three layers of 30 neurons, 30 neurons, and logsig-logsig transfer functions can be used to evaluate the mass loss and derived the masses loss during the co-combustion process. The input parameters were optimized using particle swarm optimization (PSO) to maximize the mass loss in the co-combustion process. At a temperature and heating rate of 602 K and 40 K min−1, respectively, optimization of 60% of TWC using PSO resulted in a DTGmax value of 84.868 (% min−1).