Fast characterization of biomass and waste by infrared spectra and machine learning models

Tao, Junyu; Liang, Rui; Li, Jian; Yan, Beibei; Chen, Guanyi; Cheng, Zhanjun; Li, Wanqing; Lin, Fawei; Hou, Li’an

doi:10.1016/j.jhazmat.2019.121723

Cited by 40 publications

(16 citation statements)

References 43 publications

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“…7 However, due to the lack of spectral information, the chemical composition cannot be obtained. Correspondingly, vibrational spectroscopy-based identification, including near-infrared (NIR) 8 and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), 9 has been investigated for solid waste recycling and treatment. It provides point-based spectral information of the samples rather than spatial information.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

ACS Sustainable Chem. Eng.

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Traditional methods for analyzing the biogenic and fossil carbon shares in solid waste are time-consuming and labor-intensive. A novel approach was developed to directly classify the carbon group and predict carbon content using the hyperspectral imaging (HSI) spectra of solid waste in conjunction with state-of-the-art tree-based machine learning models, including random forest (RF), extreme gradient boost, and light gradient boost machine (LGBM). All of the classifiers and regressors were able to achieve an accuracy above 0.95 and an R 2 of 0.96 in the test set, respectively. In addition, two model interpretation approaches, the Shapley additive explanation and model explainer, were applied. The results showed that the predictions of the developed models were based on a reasonable understanding of the overtone and shake of the functional groups (C–H, N–H, and O–H). Furthermore, the developed models were validated by an external test set, which did not overlap with the data used for model construction. The RF and LGBM showed robust performance with a 0.790 accuracy for carbon group classification and a 0.806 R 2 for carbon content prediction. Overall, the optimal models provided a rapid method for characterizing the biogenic carbon share in solid waste based on raw HSI spectra without preprocessing.

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Section: ■ Introductionmentioning

confidence: 99%

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

ACS Sustainable Chem. Eng.

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“…Machine learning (ML), a data mining tool for pattern discovery, has been proven to be promising for solving complicated environmental problems . Using established databases (e.g., the Phyllis2 Database for the physiochemical composition of biomass and waste) and manually extracted data from the literature, ML algorithms, such as random forest (RF) and support vector machine (SVM), have emerged as powerful tools for uncovering hidden relationships to predict solid waste categories and their properties . However, the lack of algorithm popularity is rooted in three possible reasons: (1) the limited size and low quality of data hamper the performance; , (2) poor interpretability affords a difficult information extraction process; , and (3) burdensome computation causes a long computation time.…”

Section: Introductionmentioning

confidence: 99%

“…However, the lack of algorithm popularity is rooted in three possible reasons: (1) the limited size and low quality of data hamper the performance; , (2) poor interpretability affords a difficult information extraction process; , and (3) burdensome computation causes a long computation time. Furthermore, most studies pay attention to one or at best utilize two types of models . Therefore, information about the comparative analysis of the prediction performance using different models based on the same waste data set is limited.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

Anal. Chem.

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Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R 2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R 2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

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“…Pitak et al (2021) focused on the biomass pellet production process, using machine learning for wavelength selection and PLS regression for their calibration model. Tao et al (2020) obtained IR spectra of biomass and waste with an attenuated total reflectance (ATR) and used ML for classification and characterization, employing regression techniques. Ahmed et al (2018) applied different methods for the characterization of biomass wood chips using NIR, namely ANN, Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional PLSR, with GPR showing the best results.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning approaches for spectroscopy applications

Aslanidou¹,

Soibam²

2022

Linköping Electronic Conference Proceedings

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In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

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Fast characterization of biomass and waste by infrared spectra and machine learning models

Cited by 40 publications

References 43 publications

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste

Comparison of machine learning approaches for spectroscopy applications

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