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Near-infrared spectroscopy, hyperspectral, multispectral imaging principles and applications in energy properties of biomass
(2023-08-21)
Posom, Jetsada
;
Shrestra, Bijendra
;
Maraphum, Kanvisit
;
Pitak, Lakkana
;
Saengprachatanarug, Khwantri
Biomass is renewable energy which is zero neutrality carbon energy. It is used for generating heat energy and electrical energy. Therefore, the use of biomass with high efficiency is important and the quality of biomass related to its energy should be measured before utilization and trading. The measurement of energy indexes of biomass is necessary to the thermal conversion process and the trading of biomass. However, the conventional measurement methods are laborious and take a long time, with a lot of costs. In recent years, near infrared spectroscopy (NIR) and imaging technologies (hyperspectral and multispectral images) have been widely investigated and applied as non-destructive, reliable and accurate techniques to monitor the quality and composition of biomass. This chapter contains the principle of NIR and imaging technique including essential component principles, NIR and imaging technique procedures, novel model development methods and applications. The non-destructive measurement of biomass quality as the real time and non- contact measurement will be represented. Moreover, this chapter will describe the application of NIR and imaging techniques for analysing the energy indexes of biomass, such as heating value or calorific value, proximate data, elemental composition, combustion index, pyrolysis characteristics, mechanical properties and so on.
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On-line measurement of activation energy of ground bamboo using near infrared spectroscopy
(2019-04-01)
Sirisomboon, Panmanas
;
Posom, Jetsada
On-line measurement of activation energy (Ea) is very important in supporting the thermal conversion process. The main objective of this study was to evaluate the Ea of ground bamboo using near infrared spectroscopy in real time. 80 bamboo samples with different diameters were selected using random sampling. Ea was determined using the Coats-Redfern method, and Ea of reaction order (n) at n = 1 and n≠1 was investigated. The performance of on-line measurement predicted by PLS modelling for Ea at n = 1 and Ea at n≠1 showed coefficients of determination of 0.781 and 0.714, respectively; standard error of prediction of 5.249 and 6.858 kJ/mol, respectively; and bias values of −1.0628 and −1.871 kJ/mol, respectively. Both PLS models were found to be fair and could be applied toward screening. The results showed that the vibration bands of lignocellulosic components (CH<inf>2</inf>, hemicellulose, cellulose, and lignin) highly influenced model development. Moreover, internal relationships were identified among Ea, the pre-exponential factor (A), and n, such as A (1/min) = 63251 × e0.2200×Ea (at n = 1), A (1/min) = 33719 × e0.2267×Ea (at n≠1), and n = 0.008 × Ea+0.254. These relationships can be used to evaluate A and n if Ea is known. In the case of this study, Ea was forecasted using an NIR model.
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Prediction of higher heating value, lower heating value and ash content of rice husk using FT-NIR spectroscopy
(2018-09-30)
Nakawajana, Natrapee
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Posom, Jetsada
;
Paeoui, Jaruwat
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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Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy
(2017-10-01)
Posom, Jetsada
;
Sirisomboon, Panmanas
This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g-1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g-1, -0.43%, 0.03% and -0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.
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Evaluation of lower heating value and elemental composition of bamboo using near infrared spectroscopy
(2017-01-01)
Posom, Jetsada
;
Sirisomboon, Panmanas
This study was to optimize models using near infrared spectroscopy for evaluation of lower heating value, carbon, hydrogen, nitrogen, sulfur and oxygen content of bamboo. Partial least squares regression was performed using 80 samples of bamboo where 64 samples was randomly assigned for calibration and 16 samples for validation. The result was that lower heating value and element composition did not depend on circumference size. The models showed the coefficient of determination of validation set and ratio of standard error of prediction to standard deviation of reference value of 0.934 and 3.96 for lower heating value, 0.803 and 2.31 for carbon; 0.856 and 2.65 for hydrogen; 0.973 and 6.6 for nitrogen; 0.785 and 2.19 for sulfur and 0.522 and 1.46 for oxygen, respectively. Models for lower heating value, nitrogen and hydrogen content prediction provided the highest performance, which could be used for most application. The carbon and sulfur models were fair and oxygen model was poor. The result could be a guide for applying in bamboo trading as biomass, in design and operation control of energy conversion and in predicting flue gas flow rate, air requirement, and flue gas compositions in combustion, gasification or pyrolysis.
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Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy
(2016-07-15)
Posom, Jetsada
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Shrestha, Amrit
;
Saechua, Wanphut
;
Sirisomboon, Panmanas
The MC (moisture content) and HHV (higher heating value) of Leucaena leucocephala pellets using NIR (near infrared) spectroscopy was investigated in this study. The MC of the pellets was adjusted by subjecting the samples to different relative humidity environments. The samples were scanned in diffuse reflection mode at wavenumbers of 12,500-4000 cm-1. Partial least squares regression models correlating the MC and HHV with the NIR spectra were developed and validated by full cross validation. The model for MC and HHV provided coefficients of determination (R2) of 0.995 and 0.964, a root mean square error of cross validation (RMSECV) of 0.187%wb and 79.2 J g-1, bias of -0.0008%wb and 1.29 J g-1 and a RPD (ratio of prediction to deviation) of 13.9 and 5.30, respectively. The models had excellent accuracy. This rapid quality evaluation method may be used for trading of biomass pellets. An equation related MC and HHV was also developed.

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