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

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

Energy, 2022, vol. 245, issue C

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.

Keywords: Sewage sludge; Hydrothermal carbonization; Hydrochar; Phosphorous; Machine learning; Random forest (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001980

DOI: 10.1016/j.energy.2022.123295

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