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

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

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Front. Environ. Sci. Eng. ›› 2023, Vol. 17 ›› Issue (10) : 121. DOI: 10.1007/s11783-023-1721-1
RESEARCH ARTICLE

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

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Highlights

● 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.

Abstract

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.

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Keywords

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

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Haoyang Xian, Pinjing He, Dongying Lan, Yaping Qi, Ruiheng Wang, Fan Lü, Hua Zhang, Jisheng Long. Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning. Front. Environ. Sci. Eng., 2023, 17(10): 121 https://doi.org/10.1007/s11783-023-1721-1

References

[1]
AdedejiOWangZ H (2019). Intelligent waste classification system using deep learning convolutional neural network. In: 2nd international conference on sustainable materials processing and manufacturing (SMPM). Sun City, South Africa: Procedia Manufacturing, 607–612
[2]
Altun H , Bilgil A , Fidan B C . (2007). Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications, 32(2): 599–605
CrossRef Google scholar
[3]
Breiman L . (2001). Random forests. Machine Learning, 45(1): 5–32
CrossRef Google scholar
[4]
Chakraborty S , Li B , Deb S , Paul S , Weindorf D C , Das B S . (2017). Predicting soil arsenic pools by visible near infrared diffuse reflectance spectroscopy. Geoderma, 296: 30–37
CrossRef Google scholar
[5]
Chen K , Peng Y , Lu S , Lin B , Li X . (2021). Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators. Chemosphere, 274: 129802
CrossRef Google scholar
[6]
Chen T, Guestrin C (2016). Assoc Comp M XGBoost: a Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA: Association for Computing Machinery, 785–794
[7]
Chin M Y , Lee C T , Woon K S . (2022). Policy-driven municipal solid waste management assessment using relative quadrant eco-efficiency: a case study in Malaysia. Journal of Environmental Management, 323: 116238
CrossRef Google scholar
[8]
Demetrious A , Verghese K , Stasinopoulos P , Crossin E . (2018). Comparison of alternative methods for managing the residual of material recovery facilities using life cycle assessment. Resources, Conservation and Recycling, 136: 33–45
CrossRef Google scholar
[9]
El-Fadel M , Bou-Zeid E , Chahine W , Alayli B . (2002). Temporal variation of leachate quality from pre-sorted and baled municipal solid waste with high organic and moisture content. Waste Management (New York, N.Y.), 22(3): 269–282
CrossRef Google scholar
[10]
Feng X P , Chen H M , Chen Y , Zhang C , Liu X D , Weng H Y , Xiao S P , Nie P C , He Y . (2019). Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging. Science of the Total Environment, 659: 1021–1031
CrossRef Google scholar
[11]
Garcés D , Díaz E , Sastre H , Ordóñez S , González-Lafuente J M . (2016). Evaluation of the potential of different high calorific waste fractions for the preparation of solid recovered fuels. Waste Management (New York, N.Y.), 47: 164–173
CrossRef Google scholar
[12]
Govindappa M , Tejashree S , Thanuja V , Hemashekhar B , Srinivas C , Nasif O , Pugazhendhi A , Raghavendra V B . (2021). Pomegranate fruit fleshy pericarp mediated silver nanoparticles possessing antimicrobial, antibiofilm formation, antioxidant, biocompatibility and anticancer activity. Journal of Drug Delivery Science and Technology, 61: 102289
CrossRef Google scholar
[13]
Goydaragh M G , Taghizadeh-Mehrjardi R , Jafarzadeh A A , Triantafilis J , Lado M . (2021). Using environmental variables and Fourier transform infrared spectroscopy to predict soil organic carbon. Catena, 202: 105280
CrossRef Google scholar
[14]
Higashikawa F S, Silva C A, Nunes C A, Sánchez-Monedero M A (2014). Fourier transform infrared spectroscopy and partial least square regression for the prediction of substrate maturity indexes. Science of the Total Environment, 470–471: 536–542
CrossRef Google scholar
[15]
Holmes C C , Adams N M . (2003). Likelihood inference in nearest-neighbour classification models. Biometrika, 90(1): 99–112
CrossRef Google scholar
[16]
Hoque M M , Rahman M T U . (2020). Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options. Journal of Cleaner Production, 256: 120387
CrossRef Google scholar
[17]
Huang Y C , Chen J Y , Duan Q N , Feng Y J , Luo R , Wang W J , Liu F L , Bi S F , Lee J C . (2022). A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning. Frontiers of Environmental Science and Engineering, 16(3): 38
CrossRef Google scholar
[18]
Jiang Q H , Chen Y Y , Guo L , Fei T , Qi K . (2016). Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sensing (Basel), 8(9): 755
CrossRef Google scholar
[19]
Kandlbauer L , Khodier K , Ninevski D , Sarc R . (2021). Sensor-based particle size determination of shredded mixed commercial waste based on two-dimensional images. Waste Management (New York, N.Y.), 120: 784–794
CrossRef Google scholar
[20]
Kannangara M , Dua R , Ahmadi L , Bensebaa F . (2018). Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management (New York, N.Y.), 74: 3–15
CrossRef Google scholar
[21]
Kardani N , Zhou A N , Nazem M , Lin X S . (2021). Modelling of municipal solid waste gasification using an optimised ensemble soft computing model. Fuel, 289: 119903
CrossRef Google scholar
[22]
Karimi N , Ng K T W , Richter A . (2021). Prediction of fugitive landfill gas hotspots using a random forest algorithm and Sentinel-2 data. Sustainable Cities and Society, 73: 103097
CrossRef Google scholar
[23]
Kaur G , Kaur D , Kansal S K , Garg M , Krishania M . (2022). Potential cocoa butter substitute derived from mango seed kernel. Food Chemistry, 372: 131244
CrossRef Google scholar
[24]
Li H , Liang Y , Xu Q , Cao D . (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1): 77–84
CrossRef Google scholar
[25]
Li H Y , Jia S Y , Le Z C . (2019a). Quantitative analysis of soil total nitrogen using hyperspectral imaging technology with extreme learning machine. Sensors (Basel), 19(20): 4355
CrossRef Google scholar
[26]
Li R , Gong M , Biney B W , Chen K , Xia W , Liu H , Guo A . (2022a). Three-stage pretreatment of food waste to improve fuel characteristics and incineration performance with recovery of process by-products. Fuel, 330: 125655
CrossRef Google scholar
[27]
Li X , Ma Y , Zhang M , Zhan M , Wang P , Lin X , Chen T , Lu S , Yan J . (2019b). Study on the relationship between waste classification, combustion condition and dioxin emission from waste incineration. Waste Disposal & Sustainable Energy, 1(2): 91–98
CrossRef Google scholar
[28]
Li Y , Wang Z , Guan B . (2022b). Separation and identification of nanoplastics in tap water. Environmental Research, 204: 112134
CrossRef Google scholar
[29]
Lu W J , Huo W Z , Gulina H , Pan C . (2022). Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science and Engineering, 16(9): 119
CrossRef Google scholar
[30]
Lundberg S M, Lee S I (2017). A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems. Long Beach, USA: Curran Associates Inc., 4768–4777
[31]
Michel A P M , Morrison A E , Preston V L , Marx C T , Colson B C , White H K . (2020). Rapid identification of marine plastic debris via spectroscopic techniques and machine learning classifiers. Environmental Science & Technology, 54(17): 10630–10637
CrossRef Google scholar
[32]
Mirghani M E S , Kabbashi N A , Alam M Z , Qudsieh I Y , Alkatib M F R . (2011). Rapid method for the determination of moisture content in biodiesel using FTIR spectroscopy. Journal of the American Oil Chemists’ Society, 88(12): 1897–1904
CrossRef Google scholar
[33]
Nzihou A (2020). Handbook on Characterization of Biomass, Biowaste and Related By-Products. Cham, Switzerland: Springer Cham
[34]
Paul A , Wander L , Becker R , Goedecke C , Braun U . (2019). High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil. Environmental Science and Pollution Research, 26(8): 7364–7374
CrossRef Google scholar
[35]
Peršak T , Viltuznik B , Hernavs J , Klancnik S . (2020). Vision-based sorting systems for transparent plastic granulate. Applied Sciences, 10(12): 4269
CrossRef Google scholar
[36]
Ren M H , Zhang H J , Fan Y , Zhou H Q , Cao R , Gao Y , Chen J P . (2021). Suppressing the formation of chlorinated aromatics by inhibitor sodium thiocyanate in solid waste incineration process. Science of the Total Environment, 798: 149154
CrossRef Google scholar
[37]
Said M , Amr M , Sabry Y , Khalil D , Wahba A . (2021). Plastic sorting based on MEMS FTIR spectral chemometrics sensing. Conference on Optical Sensing and Detection VI, Proceedings of SPIE, 11354: 113540J
CrossRef Google scholar
[38]
Smola A J , Schölkopf B . (2004). A tutorial on support vector regression. Statistics and Computing, 14(3): 199–222
CrossRef Google scholar
[39]
Tao J , Liang R , Li J , Yan B , Chen G , Cheng Z , Li W , Lin F , Hou L . (2020). Fast characterization of biomass and waste by infrared spectra and machine learning models. Journal of Hazardous Materials, 387: 121723
CrossRef Google scholar
[40]
Vong C M , Ip W F , Chiu C C , Wong P K . (2015). Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine. Cognitive Computation, 7(3): 381–391
CrossRef Google scholar
[41]
Wang L , Wang R . (2022). Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: a case study in lime concretion black soil. Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy, 283: 121707
CrossRef Google scholar
[42]
Wang Y , Shi Y , Zhou J , Zhao J , Maraseni T , Qian G . (2021). Implementation effect of municipal solid waste mandatory sorting policy in Shanghai. Journal of Environmental Management, 298: 113512
CrossRef Google scholar
[43]
Wang Z , Peng B , Huang Y , Sun G . (2019). Classification for plastic bottles recycling based on image recognition. Waste Management (New York, N.Y.), 88: 170–181
CrossRef Google scholar
[44]
Wold S , Sjöström M , Eriksson L . (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2): 109–130
CrossRef Google scholar
[45]
Xiao S , Dong H , Geng Y , Tian X , Liu C , Li H . (2020). Policy impacts on municipal solid waste management in Shanghai: a system dynamics model analysis. Journal of Cleaner Production, 262: 121366
CrossRef Google scholar
[46]
Xu S , Wang M , Shi X , Yu Q , Zhang Z . (2021). Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles. Science of the Total Environment, 754: 142135
CrossRef Google scholar
[47]
Xu Y , Liu J , Sun Y , Chen S , Miao X . (2023). Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection. Science of the Total Environment, 857: 159282
CrossRef Google scholar
[48]
Yan B , Liang R , Li B , Tao J , Chen G , Cheng Z , Zhu Z , Li X . (2021). Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning. Resources, Conservation and Recycling, 174: 105851
CrossRef Google scholar
[49]
Zhang C , Liu F , He Y . (2018). Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Scientific Reports, 8(1): 2166
CrossRef Google scholar
[50]
Zhang D Q , Tan S K , Gersberg R M . (2010). Municipal solid waste management in China: status, problems and challenges. Journal of Environmental Management, 91(8): 1623–1633
CrossRef Google scholar
[51]
Zhang Y , Kang S , Allen S , Allen D , Gao T , Sillanpää M . (2020). Atmospheric microplastics: a review on current status and perspectives. Earth-Science Reviews, 203: 103118
CrossRef Google scholar
[52]
Zheng K , Li Q , Wang J , Geng J , Cao P , Sui T , Wang X , Du Y . (2012). Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemometrics and Intelligent Laboratory Systems, 112: 48–54
CrossRef Google scholar
[53]
Zou H , Huang S , Ren M , Liu J , Evrendilek F , Xie W , Zhang G . (2022). Efficiency, by-product valorization, and pollution control of co-pyrolysis of textile dyeing sludge and waste solid adsorbents: their atmosphere, temperature, and blend ratio dependencies. Science of the Total Environment, 819: 152923
CrossRef Google scholar
[54]
Zou X, Zhao J, Povey M J W, Mei H, Mao H (2010). Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 667(1–2): 14–32
CrossRef Google scholar

Acknowledgements

We acknowledge the support from the National Key R&D Program of China (No. 2020YFC1910100).

Data Accessibility Statement

The data and code that support the findings of this study are available from the corresponding author, Hua Zhang, upon reasonable request.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1721-1 and is accessible for authorized users.

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