Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions

被引：0

作者：

Deng, Yizhan ^{[1
]}

Pu, Bing ^{[2
]}

Tang, Xiang ^{[3
]}

Liu, Xuran ^{[4
]}

Tan, Xiaofei ^{[1
]}

Yang, Qi ^{[1
]}

Wang, Dongbo ^{[1
]}

Fan, Changzheng ^{[1
]}

Li, Xiaoming ^{[1
]}

机构：

[1] College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education), Hunan University, Changsha

[2] College of Computer Science and Electronic Engineering, Hunan University, Changsha

[3] Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou

[4] Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, SAR, Kowloon

来源：

Chemosphere | 2024年 / 369卷

基金：

中国国家自然科学基金;

关键词：

Biochar; Graphical user interface; Machine learning; Pyrolysis; Sewage sludge;

D O I：

10.1016/j.chemosphere.2024.143812

中图分类号：

学科分类号：

摘要：

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R2 = 0.82–0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings. © 2024 Elsevier Ltd

引用

共 59 条

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