Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization

Xinzhe Zhu; Bingyou Liu; Lianpeng Sun; Ruohong Li; Huanzhong Deng; Xiefei Zhu; Daniel C W Tsang

doi:10.1016/j.biortech.2022.128454

Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization

Bioresour Technol. 2023 Feb:369:128454. doi: 10.1016/j.biortech.2022.128454. Epub 2022 Dec 9.

Authors

Xinzhe Zhu¹, Bingyou Liu², Lianpeng Sun³, Ruohong Li¹, Huanzhong Deng², Xiefei Zhu⁴, Daniel C W Tsang⁵

Affiliations

¹ School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China.
² School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
³ School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China. Electronic address: eesslp@mail.sysu.edu.cn.
⁴ Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; Department of Thermal Science and Energy Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, China.
⁵ Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.

PMID: 36503096
DOI: 10.1016/j.biortech.2022.128454

Abstract

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.

Keywords: Biochar technology; Hydrothermal carbonization; Machine learning; Municipal sludge; Sustainable waste management.

MeSH terms

Carbon*
Sewage*
Temperature

Substances

Sewage
Carbon