Near infrared hyperspectral imaging for the prediction of gaseous and particulate matter emissions from pine wood pellets

Gillespie, Gary D.; Gowen, Aoife; Finnan, John; Carroll, John P.; Farrelly, Damien J.; McDonnell, Kevin

doi:10.1016/j.biosystemseng.2019.01.001

Cited by 5 publications

(3 citation statements)

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“…The model for the prediction of the CO emissions was the weakest model of any of the models for the gaseous emissions with an R 2 value of 0.48 and RMSEP of 9.08 mg m −3 (Table 5 and Figure 2e). The loadings found to be important for the prediction of CO emissions from agricultural pellets are similar to the loadings reported and discussed in Gillespie et al 46 3.3. Prediction of Particulate Matter Emissions.…”

Section: Correlation Between Fuel Parameters and Emissions Correlatio...supporting

confidence: 86%

“…These spectral loadings for the prediction of PM 10 have been previously assigned by Gillespie, Gowen, Finnan, Carroll, Farrelly, and McDonnell. 46 As the prediction is primarily based on the presence of cellulose and hemicellulose compounds, inclusion of the region of the spectrum from 1700 to 2500 nm, which is related to the first overtone of the stretching and bending of these compounds, may allow for more accurate predictions to be made. The development of the calibration set (blue data points in Figure 3a) shows one sample as an outlier.…”

Section: Correlation Between Fuel Parameters and Emissions Correlatio...mentioning

confidence: 99%

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The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

et al. 2019

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The potential of near-infrared spectroscopy in conjunction with chemometric techniques to predict the particulate matter and gaseous emissions of biomass pellet blends was assessed in this study. A diverse range of biomass was used, including wood, Miscanthus, wheat straw, and the herbaceous energy grass Szarvasi-1 (Elymus elongatus subsp. ponticus cv. Szarvasi-1). The particulate matter emissions were predicted with root-mean-square errors of prediction (RMSEP) of 6.83 (R 2 = 0.57), 8.71 (R 2 = 0.66), and 11.25 (R 2 = 0.65) mg m–3 for the PM10, PM0, and TSP emissions, respectively. The gaseous emissions of oxides of nitrogen (NO x ), sulfur dioxide (SO2), and carbon monoxide (CO) were predicted with RMSEPs of 14.28 (R 2 = 0.93), 4.59 (R 2 = 0.88), and 9.08 (R 2 = 0.48) mg m–3, respectively. No significant models could be developed for the PM2.5 or PM1 emissions. The results indicate that near-infrared spectroscopy has the potential to predict the emissions of biomass pellets in a multibiomass stream.

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Section: Correlation Between Fuel Parameters and Emissions Correlatio...supporting

confidence: 86%

Section: Correlation Between Fuel Parameters and Emissions Correlatio...mentioning

confidence: 99%

The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

et al. 2019

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“…An NIR hyperspectral image can be used like NIR spectroscopy, but it has the advantage of high resolution and can be represented in the form of a mapping distribution, because its precision is based on pixel resolution. Two-dimensional NIR spectroscopic imaging is applied for predicting the quality of biomass pellets, such as the CV and proximate data of biofuel pellets [13]; predicting the gaseous and particulate matter emissions of pine wood pellets [20]; and assessing the MC, specific energy, and feed rate of pelleting biomass feedstock [21]. It has thus been recommended as having the potential for applica-tion in the biomass pelleting industry for real-time measurement to improve the efficiency of the system [21].…”

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confidence: 99%

Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

et al. 2021

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Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

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Sirisomboon

Saengprachatanarug

et al. 2021

Energy

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Near infrared hyperspectral imaging for the prediction of gaseous and particulate matter emissions from pine wood pellets

Cited by 5 publications

References 39 publications

The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

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