Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning  

作　　者：Haoyang Xian Pinjing He Dongying Lan Yaping Qi Ruiheng Wang Fan Lü Hua Zhang Jisheng Long 

机构地区：[1]Institute of Waste Treatment&Reclamation,College of Environmental Science and Engineering,Tongji University,Shanghai 200092,China [2]Shanghai Institute of Pollution Control and Ecological Security,Shanghai 200092,China [3]Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization,Shanghai 200092,China [4]Shanghai SUS Environment Co.,Ltd.,Shanghai 201703,China

出　　处：《Frontiers of Environmental Science & Engineering》2023年第10期41-54,共14页环境科学与工程前沿（英文）

基　　金：support from the National Key R&D Program of China(No.2020YFC1910100).

摘　　要：Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.

关 键 词：Elemental composition Infrared spectroscopy Machine learning Moisture interference Solid waste Spectral noise 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]