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Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge

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

Abstract

The hydrochar produced from hydrothermal carbonization(HTC) of sewage sludge (SS) usually has a high phosphorous (P) content, and that would result in fouling and energy efficiency reduction. Therefore, it is important to monitor the P content during the hydrochar production process. This work suggests a data-driven Random Forest-based model to predict the total P content in the hydrochar (TP-hc) from the HTC of SS. Various configurations of inputs features were examined, including the data of proximate analysis, ultimate analysis, ultimate and proximate analyses, and for each configuration, either if the total P in the SS (TP-ss) was known or not. Overall, the models including TP-ss as input have accurately predicted the TP-hc with an R2 located in [92–95%]. Features’ importance approach and partial dependence analysis pointed out that the TP-ss, ash content, reaction temperature (T), reaction time (t), and initial pH of feedwater exhibit positive effect on the TP-hc. In contrast, contribution of the volatile matter (VM) of SS was mostly negative. Dry matter loading exhibits no obvious monotonicity with TP-hc. This work could guide the production of SS-hydrochar with the desired P content, and thus avoid time and resources consuming for many trials.

Suggested Citation

  • Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001980
    DOI: 10.1016/j.energy.2022.123295
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    References listed on IDEAS

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    1. Joanna Mikusińska & Monika Kuźnia & Klaudia Czerwińska & Małgorzata Wilk, 2023. "Hydrothermal Carbonization of Digestate Produced in the Biogas Production Process," Energies, MDPI, vol. 16(14), pages 1-18, July.
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    3. Wang, Ruikun & Lin, Zhaohua & Meng, Shu & Liu, Senyang & Zhao, Zhenghui & Wang, Chunbo & Yin, Qianqian, 2022. "Effect of lignocellulosic components on the hydrothermal carbonization reaction pathway and product properties of protein," Energy, Elsevier, vol. 259(C).
    4. Djandja, Oraléou Sangué & Kang, Shimin & Huang, Zizhi & Li, Junqiao & Feng, Jiaqi & Tan, Zaiming & Salami, Adekunlé Akim & Lougou, Bachirou Guene, 2023. "Machine learning prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass," Energy, Elsevier, vol. 271(C).
    5. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    6. Cheng, Xiong & Lv, Xin & Li, Xianshan & Zhong, Hao & Feng, Jia, 2023. "Market power evaluation in the electricity market based on the weighted maintenance object," Energy, Elsevier, vol. 284(C).

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