Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition

Sirisomboon, Panmanas; Funke, Axel; Posom, Jetsada

doi:10.1016/j.renene.2019.09.128

Cited by 28 publications

(9 citation statements)

References 30 publications

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“…The scatter plots of the measured and predicted values of the validation set are shown in Figure 8a-d After comparison to other works, the result was seen to be similar to that of Feng et al [16], who reported that the SPA-PLSR model predicted the VM, FC, and ash with R 2 cal values of 0.90, 0.85, and 0.94, respectively. Our accuracy showed a similar result to that of Sirisomboon et al [43], who predicted the VM, FC, and A content of bamboo chips using Fourier Transform -Near Infrared (FT-NIR) spectroscopy, using a model that provided R 2 values of 0.81, 0.81, and 0.86, respectively.…”

Section: Results Of Model Developmentsupporting

confidence: 86%

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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Section: Results Of Model Developmentsupporting

confidence: 86%

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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“…However, the use of ground sample was complicated in terms of sample preparation, which led to higher time and cost. Posom and Sirisomboon [46] updated the NIR model in prediction of HHV, LHV, MC, VM, FC, and ash of bamboo wood chips (see Figure 4b). This model replaced the previous model (ground bamboo model).…”

Section: Proximate Analysis and Calorific Valuementioning

confidence: 99%

“…A big particle size of wood chips can affect negatively in the calibration equation because the sample was inhomogeneous; however, this problem can be solved by spectral pretreatment technique. Therefore, the direct scanning in wood chips can eliminate the need process for grinding wood samples [46]. Moreover, a calibration equation can predict ash content in bamboo wood chips successfully.…”

Section: Proximate Analysis and Calorific Valuementioning

confidence: 99%

Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy

Posom¹,

Maraphum²,

Phuphaphud³

2021

Renewable Energy - Technologies and Applications

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The parameters corresponding to combustion and pyrolysis such as proximate parameter (emissions), calorific value, elemental component, pyrolysis characteristics (temperature), and thermal properties are necessary to the thermal conversion process and the trading of biomass. Traditionally, these parameters of wood chips, milled wood, and biomass pellets are determined with chemicals, time-consuming, and required technical experts, such as thermogravimetry, bomb calorimetry, dry oven, muffle furnace, and so on. The near-infrared (NIR) spectroscopy is a rapid, noncontact no-chemical measurement. For NIR spectroscopy, only 2-3 seconds are used for evaluation, and it could be used for online measurement. The application of NIR spectroscopy in the estimation of the biomass characteristics of wood chips, milled wood, and biomass pellets is described in this chapter.

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“…Indonesia merupakan negara yang memiliki potensi biomassa yang sangat tinggi [1], akan tetapi masih belum dimanfaatkan secara maksimal. Sebagai bahan bakar khususnya bahan bakar padat nilai kalori merupakan faktor penting selain karakteristik pendukung seperti analisis ultimate dan proksimat [7], [8]. Nilai kalori biomassa secara umum terbagi dua yakni Higher Heating Value (HHV) yang didefinisan sebagai panas yang dilepaskan selama proses pemakaran dan Low Heat Value (LHV) yang didefinisan sebagai energy yang diperlukan untuk menguapkan air [9].…”

Section: Pendahuluanunclassified

Evaluasi Prediksi Nilai Higher Heating Value (HHV) Biomassa Berdasarkan Analisis Ultimate

Dirgantara

Kristian

Karelius

2019

JJMS

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Biomassa merupakan energi terbarukan yang sangat penting, dimana keberadaannya dapat menggantikan bahan bakar fosil baik padat maupun cair. Sebagai bahan bakar tentu perlu adanya analisis-analisis untuk mengetahui karakteristik dan kualitas biomassa sehingga kita dapat mengklasifikasikan biomassa yang potensial digunakan sebagai bahan bakar. Nilai kalori atau higher heating value (HHV) merupakan sifat terpenting dari suatu bahan bakar. Pada umumnya pengukuran nilai kalori menggunakan bomb calorimeter, akan tetapi pengukuran ini memerlukan waktu dan biaya sehingga tidak efektif jika yang dianalisis dalam jumlah banyak. Dalam makalah ini akan dibahas persamaan untuk memprediksi nilai HHV biomassa berdasarkan analisis ultimate yang di dapatkan dari penelitian sebelumnya. Empat prediksi nilai HHV menggunakan 15 data komposisi kimia biomassa dari penelitian sebelumnya kemudian dibandingkan berdasarkan ketepatan untuk mendapatkan prediksi terbaik. Persamaan P1 dan P4 terbaik dalam memprediksi nilai HHV berdasarkan data analisis ultimate, dimana masing-masing memprediksi terbaik di lima biomassa. Berdasarkan sumber biomassa, persamaan P1 baik dalam memprediksi hasil perkebunan dan pertanian yang berupa serabut/serat tinggi dengan kadar karbon dan oksigen yang tinggi dan tidak memiliki sulfur. P4 baik dalam memprediksi biomassa hasil sampingan kehutanan dengan karakter tinggi kadar karbon dan oksigen, rendah hydrogen dan oksigen serta memiliki sulfur.

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Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition

Cited by 28 publications

References 30 publications

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

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

Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy

Evaluasi Prediksi Nilai Higher Heating Value (HHV) Biomassa Berdasarkan Analisis Ultimate

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