Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy

Posom, Jetsada; Sirisomboon, Panmanas

doi:10.1016/j.energy.2017.01.020

Cited by 38 publications

(35 citation statements)

References 43 publications

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“…1(b)). The reproducibility of spectral data was calculated as the standard deviation of absorbance values wherein the sample was re-loaded and re-scanned 10 separate times [17]. Meanwhile, the repeatability of spectral data was calculated as the standard deviation of absorbance values where the sample was only re-scanned 10 separate times without re-loading.…”

Section: Repeatability and Reproducibilitymentioning

confidence: 99%

“…Meanwhile, the repeatability of spectral data was calculated as the standard deviation of absorbance values where the sample was only re-scanned 10 separate times without re-loading. The absorbance values at any wavelength, which presented the obvious peaks and major changes when the scanning conditions and sample conditions were varied, were used to calculate the repeatability and reproducibility [17].…”

Section: Repeatability and Reproducibilitymentioning

confidence: 99%

“…These exposures indicate that the wax on the sugarcane surface may influence the spectra of visible-shortwave near infrared. Therefore, the accuracy of spectra acquisition should be examined to design the sample preparation before modeling, such as by using the repeatability and reproducibility values [17] or principal component analysis [18]. The PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables [18].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Phuphaphud¹,

Saengprachatanarug²,

Posom³

et al. 2019

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The precision of spectroscopic methods was frequently affected by the identity of the inhomogeneous materials, especially for direct scanning. This research aimed to investigate the effects of waxy types, naturally found on sugarcane surface, on spectral characteristics. A portable Vis/SWNIR instrument with interactance mode across wavelength of 570-1031 nm was used for direct scanning the cane stalk. Principal component analysis (PCA) was applied to examine the differences in the spectra scanned from 180 samples, including 3 types of waxy type: white, black, and mixed black and white. Seven widespread pretreatments were employed to reduce the effect of the waxy types. Results show that the spectra of the samples with each waxy type was separated in groups and standard normal variate (SNV) pretreatment gave the best results. However, it was not able to eliminate the effect compared to the wax-removed samples. Meanwhile, the standard deviation of absorbance values, at the wavelength of 760, 904 and 970 nm of 3 samples, was used for assessing the repeatability and reproducibility. The samples where the waxy covers were removed provided a lower standard deviation of absorbance values of spectra than the best pretreated spectra that used SNV of the samples that retained its waxy covers by one to six times. Thus, the waxy material on cane surface should be removed before collecting spectra.

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Section: Repeatability and Reproducibilitymentioning

confidence: 99%

Section: Repeatability and Reproducibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Phuphaphud¹,

Saengprachatanarug²,

Posom³

et al. 2019

Self Cite

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“…In commercial, HHV, LHV, and A can be the important parameters in order to set price of rice husk. Waste product with higher HHV or LHV should be sold at high price, and if the biomass contains high A, biomass can be set at low price [15]. Generally, the price of rice husk in Thailand is approximately 1,300-1,400 baths per ton [16], but the price is not fair because the rice husk has different constituents.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Higher Heating Value, Lower Heating Value and Ash Content of rice Husk Using FT-NIR Spectroscopy

Nakawajana¹,

Posom²,

Paeoui³

2018

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Rice husk is the significant waste residue to be used as renewable energy. The growth of the use on rice husk for generating electricity lead to the verification of its properties. This research aimed to predict higher heating value (HHV), lower heating value (LHV), and ash content (A) of rice husk using Fourier Transform near infrared (FT-NIR) spectroscopy. Rice husk samples used in this experiment were collected from variable areas in Thailand in order to improve the model and get the robust model. The models were built using partial least squares (PLS) regression and validated by unknown sample collected from different area to calibration set. The prediction of HHV, LHV and A were represented the root mean square error of cross validation (RMSECV) of 119 J/g, 119 J/g, and 0.859%wb, respectively. The calibration model can predict the unknown sample successfully with the relative standard error of prediction (RSEP) of 1.104 %, 1.159 %, and, 5.975 %, which implied good performance of NIR model for future prediction. The results suggested that HHV, LHV, and A models should be able to assess the properties of rice husk samples and showed that NIR was reliable and suitable method for combustion system to screening material.

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“…Near infrared (NIR) spectroscopy is a nondestructive and rapid technique for estimating a quality of food and agricultural products., In addition, there have been some reports that demonstrated the use of NIR spectroscopy for the evaluation of the GCV and other properties of biomass such as the evaluation of heating value of bamboo [1], the evaluation of moisture content and GCV of Leucaena leucocephala pellets [2], the prediction of GCV of Miscanthus [3], the GCV prediction of straw [4], the evaluation of moisture content and the GCV of oil-extracted residue of Jatropha curcas [9] and the analysis of GCV and elemental compositions of sorghum [10]. The results of their studies suggested that NIR spectroscopy had the potential to estimate the calorific value of biomass fuel.…”

Section: Introductionmentioning

confidence: 99%

Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

Posom

Nakawajana

2018

MATEC Web Conf.

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Abstract. The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg -1 , which was low. Hence, the model was acceptable and cloud be used for screening.

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Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy

Cited by 38 publications

References 43 publications

Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

The Prediction of Higher Heating Value, Lower Heating Value and Ash Content of rice Husk Using FT-NIR Spectroscopy

Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

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