高光谱成像
主成分分析
人工神经网络
表征(材料科学)
人工智能
城市固体废物
燃烧热
模式识别(心理学)
计算机科学
鉴定(生物学)
生物系统
机器学习
环境科学
材料科学
工程类
废物管理
化学
燃烧
生物
植物
纳米技术
有机化学
作者
Junyu Tao,Yude Gu,Xiaoling Hao,Rui Liang,Biyu Wang,Zhanjun Cheng,Beibei Yan,Guanyi Chen
标识
DOI:10.1016/j.resconrec.2022.106731
摘要
Determining thermochemical properties and eliminating inorganic components of municipal solid waste (MSW) are crucial to its thermochemical treatment. Traditional characterization and classification technologies have shortcomings including long duration, complex operation, and inevitable sample consumption. This study proposed a hyperspectral imaging and machine learning models based method to solve these problems. Under the optimal parameter conditions, the identification accuracy of inorganic components by F1 scoring reached nearly 100% in MSW, and the prediction accuracy of carbon, hydrogen, oxygen, nitrogen contents and low heating value (LHV)of organic components by mean relative error value reached 92.6%, 86.9%, 80.4%, 54.7% and 90.5%, respectively. The results validated the hypothesis that combination of hyperspectral imaging and machine learning models are promising to accomplish fast characterization and classification of components in MSW, where principal component analysis was capable to abstract crucial information from the spectral pattern, and artificial neural network presented satisfactory classification and regression performance.
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