Deng Yizhan, Pu Bing, Tang Xiang, Liu Xuran, Tan Xiaofei, Yang Qi, Wang Dongbo, Fan Changzheng, Li Xiaoming
College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education), Hunan University, Changsha, 410082, PR China.
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, PR China.
Chemosphere. 2024 Dec;369:143812. doi: 10.1016/j.chemosphere.2024.143812. Epub 2024 Nov 29.
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 R = 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.
