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

doi:10.1007/s11783-023-1721-1

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (10) : 121 https://doi.org/10.1007/s11783-023-1721-1

RESEARCH ARTICLE

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

Haoyang Xian¹, Pinjing He^1,^2,³, Dongying Lan¹, Yaping Qi¹, Ruiheng Wang¹, Fan Lü^1,^2,³, Hua Zhang^1,^2,³(

), Jisheng Long⁴(

)

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

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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.

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

Corresponding Author(s): Hua Zhang,Jisheng Long

About author:

*These authors equally shared correspondence to this manuscript.

Issue Date: 28 April 2023

Cite this article:

Haoyang Xian,Pinjing He,Dongying Lan, et al. Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(10): 121.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1721-1
https://academic.hep.com.cn/fese/EN/Y2023/V17/I10/121

Tab.1 Specific information on the samples

Fig.1 Box plots of elemental compositions. The box boundaries indicate the 25% and 75% percentiles, and the line inside each box indicates 50%. The whiskers show other data, with the ends of each whisker representing 5% and 95%. The black points are outliers.

Fig.2 ATR-FTIR spectra of the textiles (cotton) with different moisture contents.

Element	Model		Validation		Test		Validation CV		Test CV
Element	Algorithm	Feature selection	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
C	RF	None	0.88	4.21	0.90	4.52	0.89	4.22	0.93	3.71
		SPA	0.88	4.17	0.88	4.78	0.88	4.83	0.91	4.26
		CARS	0.86	4.43	0.88	4.81	0.87	4.67	0.89	4.61
	SVR	None	0.89	3.97	0.85	5.52	0.86	5.01	0.87	5.13
		SPA	0.79	5.60	0.77	6.78	0.78	6.23	0.79	6.61
		CARS	0.84	4.81	0.83	6.19	0.82	5.67	0.83	5.92
	PLSR	None	0.80	5.25	0.72	7.59	0.74	6.61	0.79	6.46
		SPA	0.61	5.46	0.63	8.67	0.61	8.08	0.63	8.68
		CARS	0.77	5.60	0.78	6.74	0.73	6.66	0.79	6.63
	XGBOOST	None	0.89	3.91	0.88	4.90	0.88	4.41	0.92	4.01
		SPA	0.90	3.80	0.89	4.64	0.89	4.26	0.92	4.04
		CARS	0.89	4.01	0.88	4.92	0.87	4.50	0.90	4.43
	KNN	None	0.93	3.11	0.91	4.27	0.92	3.52	0.91	4.09
		SPA	0.96	2.39	0.93	3.79	0.94	2.95	0.94	3.45
		CARS	0.95	2.56	0.92	3.82	0.93	3.28	0.94	3.31
H	RF	None	0.84	0.82	0.77	1.19	0.82	0.93	0.85	0.94
		SPA	0.84	0.81	0.82	1.04	0.87	0.90	0.85	0.96
		CARS	0.85	0.78	0.84	0.97	0.84	0.89	0.87	0.87
	SVR	None	0.94	0.49	0.83	1.01	0.84	0.90	0.84	0.99
		SPA	0.88	0.71	0.82	1.05	0.81	0.94	0.84	0.98
		CARS	0.93	0.53	0.84	0.99	0.86	0.86	0.86	0.93
	PLSR	None	0.77	0.95	0.59	1.56	0.67	1.27	0.69	1.37
		SPA	0.63	1.19	0.60	1.56	0.63	1.36	0.59	1.58
		CARS	0.78	0.92	0.73	1.29	0.70	1.22	0.74	1.25
	XGBOOST	None	0.88	0.70	0.76	1.20	0.85	0.86	0.84	0.98
		SPA	0.89	0.67	0.84	0.98	0.87	0.80	0.85	0.95
		CARS	0.88	0.71	0.82	1.04	0.87	0.80	0.80	1.10
	KNN	None	0.91	0.59	0.87	0.85	0.89	0.72	0.85	0.87
		SPA	0.96	0.41	0.83	0.98	0.90	0.70	0.83	0.97
		CARS	0.96	0.42	0.97	0.70	0.91	0.66	0.89	0.63
N	RF	None	0.87	1.73	0.90	1.55	0.90	1.63	0.92	1.37
		SPA	0.89	1.59	0.92	1.40	0.91	1.56	0.94	1.16
		CARS	0.90	1.56	0.92	1.45	0.91	1.56	0.94	1.17
	SVR	None	0.97	0.91	0.93	1.29	0.93	1.32	0.97	0.92
		SPA	0.85	1.88	0.92	1.32	0.91	1.59	0.92	1.30
		CARS	0.88	1.63	0.94	1.14	0.93	1.39	0.94	1.13
	PLSR	None	0.93	1.37	0.81	2.06	0.89	1.63	0.82	2.07
		SPA	0.74	2.56	0.80	2.25	0.78	2.40	0.80	2.26
		CARS	0.89	1.69	0.87	1.85	0.89	1.64	0.89	1.70
	XGBOOST	None	0.94	1.19	0.88	1.68	0.92	1.47	0.91	1.43
		SPA	0.95	1.02	0.92	1.36	0.94	1.27	0.95	1.09
		CARS	0.94	1.24	0.90	1.61	0.93	1.29	0.93	1.34
	KNN	None	0.98	0.51	0.98	0.61	0.97	0.84	0.98	0.52
		SPA	0.99	0.44	0.97	0.78	0.98	0.75	0.98	0.56
		CARS	0.98	0.56	0.97	0.70	0.98	0.68	0.98	0.63
S	RF	None	0.89	0.24	0.79	0.30	0.84	0.28	0.79	0.30
		SPA	0.86	0.27	0.76	0.32	0.82	0.29	0.78	0.31
		CARS	0.89	0.23	0.73	0.34	0.84	0.28	0.76	0.32
	SVR	None	0.92	0.20	0.86	0.24	0.89	0.23	0.87	0.24
		SPA	0.91	0.21	0.79	0.29	0.87	0.25	0.79	0.30
		CARS	0.94	0.18	0.80	0.29	0.88	0.24	0.81	0.28
	PLSR	None	0.92	0.20	0.54	0.41	0.84	0.28	0.66	0.36
		SPA	0.84	0.29	0.72	0.35	0.79	0.32	0.72	0.35
		CARS	0.91	0.21	0.76	0.31	0.86	0.26	0.79	0.30
	XGBOOST	None	0.90	0.23	0.77	0.31	0.85	0.26	0.79	0.31
		SPA	0.88	0.24	0.76	0.32	0.85	0.27	0.79	0.30
		CARS	0.84	0.26	0.79	0.51	0.86	0.26	0.76	0.31
	KNN	None	0.95	0.15	0.83	0.26	0.89	0.23	0.84	0.25
		SPA	0.92	0.20	0.78	0.30	0.87	0.25	0.81	0.29
		CARS	0.93	0.18	0.77	0.31	0.89	0.23	0.78	0.30

Tab.2 Predicted results of the elemental compositions of solid waste based on the full spectra and on spectra with feature selection

Fig.3 Prediction results of the models. (a) Comparison of model R²_val and R²_test values using full spectra with or without feature selection. (b) Variation of R² with different noise factors for the validation set. (c) Variation of R² with different noise factors for the test set.

Element	Model		Validation		Test		Validation CV		Test CV
Element	Algorithm	Feature selection	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
C	RF	None	0.90	3.21	0.90	3.37	0.90	3.18	0.90	3.31
		SPA	0.90	3.25	0.89	3.46	0.90	3.19	0.89	3.45
		CARS	0.90	3.22	0.89	3.39	0.89	3.18	0.90	3.32
	SVR	None	0.93	2.77	0.92	2.97	0.91	2.87	0.92	2.97
		SPA	0.93	2.76	0.91	3.17	0.91	2.95	0.91	3.12
		CARS	0.93	2.75	0.92	3.04	0.91	2.90	0.92	3.03
	PLSR	None	0.93	2.75	0.91	3.17	0.90	3.08	0.90	3.35
		SPA	0.91	2.98	0.88	3.57	0.89	3.35	0.89	3.53
		CARS	0.91	2.44	0.93	2.78	0.91	3.14	0.87	3.71
	XGBOOST	None	0.91	3.16	0.87	3.76	0.90	3.18	0.88	3.55
		SPA	0.92	2.88	0.88	3.59	0.89	3.22	0.89	3.39
		CARS	0.91	3.14	0.87	3.71	0.89	3.26	0.89	3.52
	KNN	None	0.88	3.53	0.87	3.66	0.88	3.51	0.90	3.35
		SPA	0.90	3.35	0.88	3.58	0.88	3.48	0.88	3.52
		CARS	0.90	3.26	0.88	3.65	0.87	3.56	0.89	3.45
H	RF	None	0.88	0.46	0.88	0.48	0.88	0.45	0.88	0.47
		SPA	0.89	0.46	0.87	0.50	0.88	0.46	0.87	0.49
		CARS	0.87	0.49	0.87	0.50	0.86	0.49	0.86	0.50
	SVR	None	0.87	0.48	0.87	0.48	0.89	0.44	0.87	0.48
		SPA	0.91	0.59	0.87	0.43	0.91	0.39	0.91	0.42
		CARS	0.85	0.50	0.86	0.50	0.88	0.45	0.87	0.50
	PLSR	None	0.92	0.39	0.87	0.49	0.88	0.45	0.89	0.45
		SPA	0.91	0.41	0.86	0.51	0.87	0.48	0.86	0.51
		CARS	0.94	0.33	0.93	0.36	0.92	0.36	0.93	0.36
	XGBOOST	None	0.91	0.41	0.86	0.51	0.89	0.45	0.86	0.51
		SPA	0.90	0.44	0.86	0.51	0.88	0.46	0.86	0.51
		CARS	0.89	0.45	0.83	0.56	0.86	0.49	0.86	0.52
	KNN	None	0.90	0.44	0.81	0.59	0.88	0.46	0.87	0.48
		SPA	0.90	0.43	0.85	0.53	0.88	0.47	0.87	0.51
		CARS	0.90	0.43	0.85	0.59	0.88	0.47	0.86	0.52
N	RF	None	0.95	1.36	0.93	1.56	0.95	1.35	0.94	1.42
		SPA	0.97	1.02	0.93	1.62	0.96	1.13	0.92	1.52
		CARS	0.96	1.26	0.94	1.49	0.96	1.25	0.94	1.42
	SVR	None	0.97	0.95	0.97	0.98	0.98	0.90	0.98	0.91
		SPA	0.99	0.76	0.99	0.78	0.98	0.77	0.98	0.78
		CARS	0.98	0.84	0.98	0.81	0.98	0.84	0.98	0.80
	PLSR	None	0.95	1.10	0.95	1.40	0.95	1.38	0.95	1.33
		SPA	0.91	1.93	0.91	1.85	0.91	1.78	0.91	1.84
		CARS	0.96	1.15	0.96	1.25	0.96	1.17	0.96	1.25
	XGBOOST	None	0.96	1.26	0.92	1.67	0.95	1.28	0.90	1.82
		SPA	0.97	1.00	0.94	1.38	0.97	1.04	0.93	1.64
		CARS	0.96	1.22	0.95	1.41	0.96	1.23	0.95	1.30
	KNN	None	0.98	0.77	0.98	0.93	0.98	0.85	0.97	0.99
		SPA	0.99	0.71	0.98	0.84	0.98	0.77	0.98	0.85
		CARS	0.98	0.85	0.98	0.95	0.98	0.84	0.98	0.82
S	RF	None	0.64	0.32	0.59	0.29	0.56	0.33	0.69	0.27
		SPA	0.48	0.39	0.50	0.33	0.47	0.37	0.60	0.31
		CARS	0.63	0.33	0.60	0.30	0.61	0.32	0.70	0.28
	SVR	None	0.71	0.29	0.62	0.29	0.76	0.24	0.86	0.19
		SPA	0.93	0.14	0.91	0.14	0.92	0.13	0.93	0.13
		CARS	0.89	0.17	0.87	0.17	0.89	0.16	0.94	0.12
	PLSR	None	0.87	0.19	0.89	0.16	0.86	0.18	0.92	0.14
		SPA	0.69	0.30	0.80	0.22	0.76	0.26	0.69	0.28
		CARS	0.89	0.17	0.90	0.16	0.88	0.17	0.90	0.16
	XGBOOST	None	0.61	0.33	0.56	0.31	0.53	0.35	0.54	0.33
		SPA	0.65	0.32	0.64	0.27	0.55	0.35	0.49	0.36
		CARS	0.72	0.29	0.72	0.25	0.63	0.32	0.74	0.26
	KNN	None	0.64	0.26	0.47	0.34	0.55	0.35	0.59	0.27
		SPA	0.59	0.33	0.54	0.29	0.64	0.30	0.45	0.34
		CARS	0.73	0.23	0.59	0.29	0.70	0.26	0.45	0.34

Tab.3 Predicted results for elemental compositions of solid waste based on the full spectra and spectra with feature selection under moisture interference

Fig.4 Prediction results under moisture interference. (a) Scatter plots of predicted and measured values for the validation set. (b) Scatter plots of predicted and measured values for the test set.

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