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Experimental study of the co-pyrolysis of sewage sludge and wet waste via TG-FTIR-GC and artificial neural network model: Synergistic effect, pyrolysis kinetics and gas products

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  • Sun, Hao
  • Bi, Haobo
  • Jiang, Chunlong
  • Ni, Zhanshi
  • Tian, Junjian
  • Zhou, Wenliang
  • Qiu, Zhicong
  • Lin, Qizhao

Abstract

This study investigated the co-pyrolysis characteristics and kinetics of sewage sludge (SS) and wet waste (WW) using thermogravimetric-Fourier transform infrared spectrometry-gas chromatography/mass spectrometry (TG-FTIR-GC/MS) and artificial neural network (ANN). The proportion of WW is 0, 10, 30, 50, 70, and 100%, respectively. These mixtures were heated from 30 to 900 °C at three heating rates (10, 20, and 40 °C/min). The change of gas functional groups with different blends (-OH, –CH, CO2, CC, phenol, CO, and NH3) was detected by FTIR. S3W7 has a synergistic effect on the pyrolysis in all temperature ranges and can also greatly suppress CO2 emission (−35.25%), which is of practical significance to carbon neutrality. S3W7 was recommended as the best ratio. The gas products of S3W7 were obtained by GC/MS, which were mainly nitrides (C5H5N, C4H11N, etc.), hydrocarbons containing CO (C3H6O2, C7H8O2, etc.), and furans (C5H6O2, C6H8O, etc.). The apparent activation energy (E) was measured using Flynne-Walle-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods. Machine learning methods were used to analyze the pyrolysis. ANN19 was found the best prediction model of 21 models. The equation to predict TG data was established.

Suggested Citation

  • Sun, Hao & Bi, Haobo & Jiang, Chunlong & Ni, Zhanshi & Tian, Junjian & Zhou, Wenliang & Qiu, Zhicong & Lin, Qizhao, 2022. "Experimental study of the co-pyrolysis of sewage sludge and wet waste via TG-FTIR-GC and artificial neural network model: Synergistic effect, pyrolysis kinetics and gas products," Renewable Energy, Elsevier, vol. 184(C), pages 1-14.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:1-14
    DOI: 10.1016/j.renene.2021.11.049
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