期刊
ENERGY
卷 261, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.125238
关键词
Coal slime; Lignin; TG-FTIR; Co-pyrolysis; Artificial neural network; Principal component analysis
资金
- National Natural Science Foundation of China [51376171]
- National Key R & D Program of China [2021YFF0601004]
Thermogravimetric-Fourier transform infrared spectrometry was used to investigate the co-pyrolysis of coal slime and lignin in this study. The composition and evolution of gaseous products during the pyrolysis process were analyzed, as well as the influence of lignin content. An artificial neural network prediction model with a relative error less than 2.5% was established.
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.
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