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Thermogravimetric analysis of co-combustion characteristics of sewage sludge and bamboo scraps combined with artificial neural networks

Author

Listed:
  • Liu, Xiang
  • Bi, Haobo
  • Tian, Junjian
  • Ni, Zhanshi
  • Shi, Hao
  • Yao, Yurou
  • Meng, Kesheng
  • Wang, Jian
  • Lin, Qizhao

Abstract

The increasing amount of sludge resources has brought great challenges to the ecological environment and sustainable development. Co-combustion of low calorific value sewage sludge and biosolid waste has become a new and effective way to treat sludge. The co-combustion characteristics of sewage sludge and bamboo scraps in air were studied by thermogravimetric mass spectrometry and artificial neural network. The thermogravimetric analysis of combustion at three different heating rates was performed with five different mixing ratios. Through comprehensive combustion index and cooperative analysis, it is concluded that the co-burning effect and cooperative effect of sludge and bamboo are better. Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose were used to calculate the apparent activation energy. The average activation energy of the sludge mixed with 75% bamboo scraps was the lowest (126.3 kJ mol−1) under the co-combustion condition. The ion current of combustion gas products during combustion was studied by mass spectrometry. Co-combustion can increase NOx and reduce SO2, and 75% bamboo slag mixed sludge has the least gas production. Finally, the prediction model of sludge co-combustion is established by artificial neural network, which provides a prospective guidance for the resource utilization of solid waste.

Suggested Citation

  • Liu, Xiang & Bi, Haobo & Tian, Junjian & Ni, Zhanshi & Shi, Hao & Yao, Yurou & Meng, Kesheng & Wang, Jian & Lin, Qizhao, 2024. "Thermogravimetric analysis of co-combustion characteristics of sewage sludge and bamboo scraps combined with artificial neural networks," Renewable Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:renene:v:226:y:2024:i:c:s0960148124004038
    DOI: 10.1016/j.renene.2024.120338
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    References listed on IDEAS

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