TY - JOUR
T1 - Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization
AU - Zhu, Xinzhe
AU - Liu, Bingyou
AU - Sun, Lianpeng
AU - Li, Ruohong
AU - Deng, Huanzhong
AU - Zhu, Xiefei
AU - Tsang, Daniel C.W.
N1 - Funding Information:
The authors appreciate the financial support from National Natural Science Foundation of China (No. 52200112) and Environment and Conservation Fund (Project 101/2020) for this study.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2
Y1 - 2023/2
N2 - In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R2 of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.
AB - In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R2 of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.
KW - Biochar technology
KW - Hydrothermal carbonization
KW - Machine learning
KW - Municipal sludge
KW - Sustainable waste management
UR - http://www.scopus.com/inward/record.url?scp=85144050884&partnerID=8YFLogxK
U2 - 10.1016/j.biortech.2022.128454
DO - 10.1016/j.biortech.2022.128454
M3 - Journal article
C2 - 36503096
AN - SCOPUS:85144050884
SN - 0960-8524
VL - 369
JO - Bioresource Technology
JF - Bioresource Technology
M1 - 128454
ER -