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

Abstract

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

● Feature selection methods were used to improve models’ prediction accuracy.

● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97.

● Some suitable models showed insensitivity to spectral noise.

● Under moisture interference, the models still had good prediction performance.

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.

Graphical abstract

Keywords

Elemental composition / Infrared spectroscopy / Machine learning / Moisture interference / Solid waste / Spectral noise

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