Prediction of biomass pellet quality indices using near infrared spectroscopy

Gillespie, Gary D.; Everard, Colm; McDonnell, Kevin

doi:10.1016/j.energy.2014.12.014

Cited by 50 publications

(31 citation statements)

References 30 publications

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“…Figure 3 reports the most relevant peaks in the prediction of Ac for the woodchip dataset. This confirms the results in literature 15,28 : Several bands are related to O-H bond, in particular peaks at 6858, 5782, 4991, and 4748 cm −1 . More specifically, the peak at 5782 cm −1 is assigned to first overtone O-H of lignin and 4748 cm −1 to O-H deformation + O−H…”

Section: Interpreting the Modelssupporting

confidence: 93%

“…According to Lestander and Rhen, the prediction for Ac is mainly based on interactions in carbon chains. Gillespie et al report the same findings, confirming that the prediction of ash is related to the presence of C–H and O–H bond containing compounds.…”

Section: Resultsmentioning

confidence: 56%

“…13 It allows to separate the chemical light absorption from the physical light scatter. Literature has already showed that MSC could be used for data preprocessing of biofuels [14][15][16][17] and feed 18,19 matrices. The basic idea behind MSC has been extended into more elaborate augmentations; one of the most common is the extended multiplicative scatter correction (EMSC).…”

Section: Introductionmentioning

confidence: 99%

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Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors

Mancini

Toscano

Rinnan

2019

Journal of Chemometrics

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Scattering effect is a really common physical phenomenon during near‐infrared analysis. It is an undesired variation in the spectral data due to a deviation of light from a straight trajectory into different paths. The nonlinearities introduced can be handled by using spectral preprocessing techniques. The situation is completely different when the parameter of interest is physical by nature, such as ash content, in this case removing the physical artifacts of scattering would be negative for the final model. In this study, we have decided to investigate the ash content parameter trying to figure out if the information useful for its prediction is related to the scattering effects, the chemical features, or a mixture of them. To this aim, two near‐infrared spectral datasets were taken into consideration: woodchip for energy sector and pellet samples for feed sector. A new regression model (CORR‐PLS) was developed by including principal components analysis scores and extended multiplicative scatter correction (EMSC) factors as physical parameters into the partial least squares (PLS) regression model. The prediction performance of the regular PLS models (PLS on the raw data and MSC pre‐treated data) were compared with that of the CORR‐PLS model both with regard to prediction uncertainty and model complexity in order to evaluate which is the relevant information for prediction of the ash content.

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Section: Interpreting the Modelssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors

Mancini

Toscano

Rinnan

2019

Journal of Chemometrics

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“…Thus, NIR spectroscopy has great potential to measure the moisture of the samples due to presence of O-H in water. NIR spectroscopy was applied for the evaluation of moisture content of rice straw by Jin and Chen [17], Miscanthus x giganteus and short rotational coppice willow by Fagan et al [18], biomass pellet by Gillespie et al [19]. NIR spectroscopy has been used not only to measure the moisture content of biomass but also the composition of biomass like cellulose, hemicellulose, lignin [20,21], ash [18], heating value [22].…”

Section: Introductionmentioning

confidence: 99%

Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy

Shrestha

Sirisomboon

2018

MATEC Web Conf.

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Moisture in biomass plays important role during storage, combustion and pelletization. In order to measure moisture content in bamboo chips, two diode array near-infrared instruments, NIR-Gun (600-1100 nm at 2 nm intervals) and Micro-NIR (1150-2150 nm at 7 nm intervals), were used for scanning bamboo chips. Total number of samples used for developing model after removing outliers was 252. The circumference and moisture content of bamboos used were in the range between 16-39 cm and 39-86% wet basis (wb) respectively. Partial least squares regression technique was used to develop the model to predict the moisture content in bamboo chips. The R2, SECV, SEP, bias and RPD of optimum model of NIR-Gun were found to be 0.924, 2.871% wb, 2.385% wb, -0.250% wb and 3.656, while for Micro-NIR model the values were found to be 0.743, 4.349% wb, 4.499% wb, 0.026% wb and 1.972 respectively. In prediction of moisture content in bamboo chip, both models show the effect of different constituents of bamboo more than moisture. This study indicates that the results are suitable for screening the moisture content in bamboo chips. This would be helpful for process controlling using the moisture parameter during drying, pelletization and thermochemical conversion.

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“…urante a última década, a análise de espectroscopia no infravermelho próximo (NIR) surgiu como uma ferramenta analítica confiável e promissora para a avaliação da qualidade dos atributos do café, como também de outras biomassas (Gillespie et al, 2015;Barbin et al, 2014;Rambo et al,2013). A espectroscopia NIR tem um enorme potencial para a obtenção de informações rápidas sobre a composição química do café, além da capacidade de quantificar algumas características importantes, tais como umidade, cinzas, teor de carbono fixo, material volátil entre inúmeros outros.…”

Section: Introductionunclassified

Predição Por Calibração Multivariada Dos Parâmetros De Qualidade De Biomassas De Café

Rambo

Macêdo

Silva

et al. 2015

CeN

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Resumo A previsão em tempo real dos parâmetros de qualidade das biomassas de café permite uma maior eficácia nas indústrias e no seu processo de produção de energia, permitindo a determinação de múltiplos constituintes químicos e evitando simultaneamente a extensa preparação da amostra. Modelos de calibração multivariada utilizando espectros obtidos pelo infravermelho próximo (NIR) foram desenvolvidos e aplicados para prever as seguintes propriedades do café: umidade, cinzas, carbono fixo e teor de voláteis. Resultados satisfatórios foram obtidos demonstrando a importância da metodologia sob o ponto de vista econômico, mas também ambiental, já que resíduos da indústria de café (cascas) foram avaliados e demonstraram potencial de aproveitamento.

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Prediction of biomass pellet quality indices using near infrared spectroscopy

Cited by 50 publications

References 30 publications

Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors

Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors

Rapid evaluation of moisture content in bamboo chips using diode array near infrared spectroscopy

Predição Por Calibração Multivariada Dos Parâmetros De Qualidade De Biomassas De Café

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