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A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge

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  • Djandja, Oraléou Sangué
  • Duan, Pei-Gao
  • Yin, Lin-Xin
  • Wang, Zhi-Cong
  • Duo, Jia

Abstract

In this work, 138 datapoints, including elemental composition and ultimate analysis of various types of sewage sludge, and the hydrothermal carbonization reaction conditions, are used to develop a prediction model for the nitrogen content of the hydrochar. The results suggested that a two-layer feedforward neural network with five (05) neurons in the hidden layer can accurately predict the nitrogen content of the hydrochar based on the reaction temperature and the contents of nitrogen, carbon, volatiles and fixed carbon in the feedstock. Over 100 runs, the R2 and RMSE are in [87.547–99.097%] and [0.243–1.431] wt.% (db), respectively. Moreover, a statistical and regression analysis revealed that the sewage sludge-N is the main contributor to the hydrochar-N. Mostly, 40–70% of sewage sludge-N goes to hydrochar-N. The results are consistent with previous experimental reports, and this model can be used to predict the sewage sludge-derived hydrochar-N.

Suggested Citation

  • Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012585
    DOI: 10.1016/j.energy.2021.121010
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    1. Małgorzata Wilk & Marcin Gajek & Maciej Śliz & Klaudia Czerwińska & Lidia Lombardi, 2022. "Hydrothermal Carbonization Process of Digestate from Sewage Sludge: Chemical and Physical Properties of Hydrochar in Terms of Energy Application," Energies, MDPI, vol. 15(18), pages 1-17, September.
    2. Leng, Lijian & Zhou, Junhui & Zhang, Weijin & Chen, Jiefeng & Wu, Zhibin & Xu, Donghai & Zhan, Hao & Yuan, Xingzhong & Xu, Zhengyong & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2024. "Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes," Energy, Elsevier, vol. 288(C).

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