Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability

被引：9

作者：

Sun, Lianpeng ^{[1
,2
]}

Li, Mingxuan ^{[1
]}

Liu, Bingyou ^{[1
]}

Li, Ruohong ^{[1
,2
]}

Deng, Huanzhong ^{[1
]}

Zhu, Xiefei ^{[3
]}

Zhu, Xinzhe ^{[1
,2
]}

Tsang, Daniel C. W. ^{[4
]}

机构：

[1] Sun Yat Sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China

[2] Sun Yat Sen Univ, Guangdong Prov Key Lab Environm Pollut Control & R, Guangzhou 510275, Peoples R China

[3] Sun Yat Sen Univ Shenzhen, Sch Adv Energy, Shenzhen 518107, Peoples R China

[4] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Clear Water Bay, Hong Kong, Peoples R China

来源：

BIORESOURCE TECHNOLOGY | 2024年 / 394卷

基金：

中国国家自然科学基金;

关键词：

Municipal sludge; Thermochemical treatment; Machine learning; Sustainable waste management; Carbon storage/utilization; SEWAGE-SLUDGE; COFFEE GROUNDS; THERMOGRAVIMETRIC ANALYSIS; ENERGY RECOVERY; PYROLYSIS; BIOMASS; COCOMBUSTION; GASIFICATION; TEMPERATURE; COMBUSTION;

D O I：

10.1016/j.biortech.2023.130254

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

引用

页数：13

共 86 条

[1] Comparison of the slow, fast, and flash pyrolysis of recycled maize-cob biomass waste, box-benhken process optimization and characterization studies for the thermal fast pyrolysis production of bio-energy [J].

Adelawon, B. O. ;

Latinwo, G. K. ;

Eboibi, B. E. ;

Agbede, O. O. ;

Agarry, S. E. .

CHEMICAL ENGINEERING COMMUNICATIONS, 2022, 209 (09) :1246-1276

[2] Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge [J].

Adibimanesh, Behrouz ;

Polesek-Karczewska, Sylwia ;

Bagherzadeh, Faramarz ;

Szczuko, Piotr ;

Shafighfard, Torkan .

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2023, 56

[3] Best practices in machine learning for chemistry comment [J].

Artrith, Nongnuch ;

Butler, Keith T. ;

Coudert, Francois-Xavier ;

Han, Seungwu ;

Isayev, Olexandr ;

Jain, Anubhav ;

Walsh, Aron .

NATURE CHEMISTRY, 2021, 13 (06) :505-508

[4] Machine learning methods for modelling the gasification and pyrolysis of biomass and waste [J].

Ascher, Simon ;

Watson, Ian ;

You, Siming .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 155

[5] Relation between prognostics predictor evaluation metrics andlocal interpretability SHAP values [J].

Baptista, Marcia L. ;

Goebel, Kai ;

Henriques, Elsa M. P. .

ARTIFICIAL INTELLIGENCE, 2022, 306

[6] Thermodynamics, kinetics, gas emissions and artificial neural network modeling of co-pyrolysis of sewage sludge and peanut shell [J].

Bi, Haobo ;

Wang, Chengxin ;

Jiang, Xuedan ;

Jiang, Chunlong ;

Bao, Lin ;

Lin, Qizhao .

FUEL, 2021, 284

[7] Progress in thermochemical co-processing of biomass and sludge for sustainable energy, value-added products and circular economy [J].

Chan, Yi Herng ;

Lock, Serene Sow Mun ;

Chin, Bridgid Lai Fui ;

Wong, Mee Kee ;

Loy, Adrian Chun Minh ;

Foong, Shin Ying ;

Yiin, Chung Loong ;

Lam, Su Shiung .

BIORESOURCE TECHNOLOGY, 2023, 380

[8] Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning [J].

Chen, Chao ;

Wang, Zhi ;

Ge, Yadong ;

Liang, Rui ;

Hou, Donghao ;

Tao, Junyu ;

Yan, Beibei ;

Zheng, Wandong ;

Velichkova, Rositsa ;

Chen, Guanyi .

BIORESOURCE TECHNOLOGY, 2023, 377

[9] Visualizing a field of research: A methodology of systematic scientometric reviews [J].

Chen, Chaomei ;

Song, Min .

PLOS ONE, 2019, 14 (10)

[10] Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling [J].

Chen, Jiacong ;

Xie, Candie ;

Liu, Jingyong ;

He, Yao ;

Xie, Wuming ;

Zhang, Xiaochun ;

Chang, Kenlin ;

Kuo, Jiahong ;

Sun, Jian ;

Zheng, Li ;

Sun, Shuiyu ;

Buyukada, Musa ;

Evrendilek, Fatih .

BIORESOURCE TECHNOLOGY, 2018, 250 :230-238

← 1 2 3 4 5 6 7 8 9 →