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Research on the co-pyrolysis of coal slime and lignin based on the combination of TG-FTIR, artificial neural network, and principal component analysis

Author

Listed:
  • Ni, Zhanshi
  • Bi, Haobo
  • Jiang, Chunlong
  • Sun, Hao
  • Zhou, Wenliang
  • Qiu, Zhicong
  • He, Liqun
  • Lin, Qizhao

Abstract

Confronted with the shortage of fossil energy, the large inventory and the serious pollution of industrial solid waste, the development of clean and efficient industrial solid waste disposal methods have become a trend. In this study, Thermogravimetric-Fourier transform infrared spectrometry was utilized to carry out the co-pyrolysis experiment of coal slime and lignin. Pyrolysis experiments were carried out following 7 different mass mixing ratios. The initial pyrolysis temperatures of CS, S9G1, S7G3, S5G5, S3G7, S1G9, and LIG were 414.5, 373.3, 287.6, 233.3, 225.8, 218.6, and 209.6 °C, respectively. By observing the evolution of the gaseous products of the sample pyrolysis, the results showed that the gaseous products mainly include hydrocarbons, aldehydes, ethers, and alcohols. The ratio of lignin in the mixture was changed, and the interaction between the sample particles was different. The principal component analysis method provided helps to understand the mechanism of co-pyrolysis of coal slime and lignin. The relative error of the established artificial neural network prediction was less than 2.5%. This paper comprehensively analyzed the interaction and gas evolution law during the co-pyrolysis of coal slime and lignin.

Suggested Citation

  • Ni, Zhanshi & Bi, Haobo & Jiang, Chunlong & Sun, Hao & Zhou, Wenliang & Qiu, Zhicong & He, Liqun & Lin, Qizhao, 2022. "Research on the co-pyrolysis of coal slime and lignin based on the combination of TG-FTIR, artificial neural network, and principal component analysis," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021260
    DOI: 10.1016/j.energy.2022.125238
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    References listed on IDEAS

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    1. Xie, Candie & Liu, Jingyong & Zhang, Xiaochun & Xie, Wuming & Sun, Jian & Chang, Kenlin & Kuo, Jiahong & Xie, Wenhao & Liu, Chao & Sun, Shuiyu & Buyukada, Musa & Evrendilek, Fatih, 2018. "Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks," Applied Energy, Elsevier, vol. 212(C), pages 786-795.
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    Cited by:

    1. Jiang, Xu & Xu, Jun & He, Qichen & Wang, Cong & Jiang, Long & Xu, Kai & Wang, Yi & Su, Sheng & Hu, Song & Du, Zhenyi & Xiang, Jun, 2023. "A study of the relationships between coal heterogeneous chemical structure and pyrolysis behaviours: Mechanism and predicting model," Energy, Elsevier, vol. 282(C).

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