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Combustion performance of fine screenings from municipal solid waste: Thermo-kinetic investigation and deep learning modeling via TG-FTIR

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  • Tian, Lu
  • Lin, Kunsen
  • Zhao, Youcai
  • Zhao, Chunlong
  • Huang, Qifei
  • Zhou, Tao

Abstract

The combustion behavior, kinetics, thermodynamics and gas products of fine screenings (FS) classified from municipal solid waste (MSW) in an air atmosphere were explored by TG-FTIR. A deep learning model was established using 1D–CNN–LSTM algorithm to predict thermogravimetric data of FS combustion, with visualization technology (TensorBoard) applied to display the weights and biases in various cells. The thermogravimetric analysis (TG) and differential thermal gravity (DTG) curves indicated that the FS combustion process can be divided into four stages. The average activation energy (Ea) of FS combusted at different stages, exhibited different change tendencies with increasing levels of conversion (α). The highest enthalpy (ΔH) of 206.40 kJ/mol and free Gibbs energy (ΔG) of 55.03 kJ/mol emerged in stage Ⅳ, while the highest changes of entropy (ΔS) of 169.11 J/(mol·K) occurred in stage Ⅱ. The main gas products (CO2, H2O and CO) and functional groups (CO and phenols) were all detected. For the 1D–CNN–LSTM model, the optimal settings for the prediction of thermogravimetric data were a neuron number of 150, dropout of 0.003, epoch number of 200, and batch size of 25. The highest correlation coefficient (R2) of 94.41% was obtained using the optimum model parameters, achieving an excellent prediction performance.

Suggested Citation

  • Tian, Lu & Lin, Kunsen & Zhao, Youcai & Zhao, Chunlong & Huang, Qifei & Zhou, Tao, 2022. "Combustion performance of fine screenings from municipal solid waste: Thermo-kinetic investigation and deep learning modeling via TG-FTIR," Energy, Elsevier, vol. 243(C).
  • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221030322
    DOI: 10.1016/j.energy.2021.122783
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    References listed on IDEAS

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    1. Dastjerdi, B. & Strezov, V. & Kumar, R. & Behnia, M., 2019. "An evaluation of the potential of waste to energy technologies for residual solid waste in New South Wales, Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    2. 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.
    3. Li, Wei & Yuan, Zhihang & Chen, Xiaoliang & Wang, Hui & Wang, Luochun & Lou, Ziyang, 2021. "Green refuse derived fuel preparation and combustion performance from the solid residues to build the zero-waste city," Energy, Elsevier, vol. 225(C).
    4. Deng, Banglin & Li, Qing & Chen, Yangyang & Li, Meng & Liu, Aodong & Ran, Jiaqi & Xu, Ying & Liu, Xiaoqiang & Fu, Jianqin & Feng, Renhua, 2019. "The effect of air/fuel ratio on the CO and NOx emissions for a twin-spark motorcycle gasoline engine under wide range of operating conditions," Energy, Elsevier, vol. 169(C), pages 1202-1213.
    5. Bi, Haobo & Wang, Chengxin & Lin, Qizhao & Jiang, Xuedan & Jiang, Chunlong & Bao, Lin, 2020. "Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR," Energy, Elsevier, vol. 213(C).
    6. Hasan, M.M. & Rasul, M.G. & Khan, M.M.K. & Ashwath, N. & Jahirul, M.I., 2021. "Energy recovery from municipal solid waste using pyrolysis technology: A review on current status and developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
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    1. Zhang, Xuefei & Li, Yongling & Zhang, Xianwen & Ma, Peiyong & Xing, Xianjun, 2023. "Co-combustion of municipal solid waste and hydrochars under non-isothermal conditions: Thermal behaviors, gaseous emissions and kinetic analyses by TGA–FTIR," Energy, Elsevier, vol. 265(C).

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