Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy

Posom, Jetsada; Shrestha, Amrit; Saechua, Wanphut; Sirisomboon, Panmanas

doi:10.1016/j.energy.2016.04.041

Cited by 50 publications

(19 citation statements)

References 29 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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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“…There is a direct and inversely proportional relationship between moisture content and HHV. This occurs since the energy released during combustion is used in the sample drying process (Posom et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Different Types of Lignocellulosic Materials for Energy Generation in the Ceramic Industry

et al. 2019

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This work aims at the physicochemical characterization of four species: Eucalyptus sp, Pinus sp, Citrus sinensis and Hevea brasiliensis for use in ceramic furnace. Immediate analysis, chemical analyses were carried out (total extractives, Klason lignin, holocellulose and alpha-cellulose content). Results were applied to ANOVA and Tukey for statistics. The ash content of Pinus sp was 1.60%, for volatile material content Eucalyptus sp presented 83.61%, for fixed carbon values, Citrus sinensis presented 20.03%. Chemical analyses in the total extractive content, Citrus sinensis presented 21.76%, Klason lignin content, Pinus sp had 39.24%, Eucalyptus sp 60.29% had the highest holocellulose and alpha-cellulose, which was 42.72%. Pinus sp sample was the one with the highest heating value of 20.090 J/g. According to results obtained in the analyses, it is possible to conclude that all species have potential for applications in ceramic furnace.

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“…Near infrared spectroscopy (NIRs) have been used to evaluate properties of biomass in many researches. GCV of various biomass was successfully predicted using NIRs such as millet [9], leucaena leucocephala [10], jatropha curcas kernels [11], and straw [12]. In addition, Fagen et al [13] studied application of NIRs for measuring moisture content, gross calorific value (GCV), carbon (C) and ash content (AC) of two dedicated bioenergy crops which results of their showed performance of NIRs technique to evaluate these properties.…”

Section: Introductionmentioning

confidence: 99%

Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

2018

Self Cite

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The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.

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Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy

Cited by 50 publications

References 29 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

Different Types of Lignocellulosic Materials for Energy Generation in the Ceramic Industry

Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

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