Real-time measuring energy characteristics of cane bagasse using NIR spectroscopy

Posom, Jetsada; Phuphaphud, Arthit; Saengprachatanarug, Khwantri; Maraphum, Kanvisit; Saijan, Sawitree; Pongkan, Kanokwan; Srimai, Kedkanok

doi:10.1016/j.sbsr.2022.100519

Cited by 5 publications

(8 citation statements)

References 30 publications

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“…The NIR spectra of each biomass sample contained multiple independent variables, resulting in collinearity issues, redundant information, and heightened computational complexity [20]. To tackle these challenges, uninformative wavelength variables, having either negligible or adverse impacts on model performance, need identification and elimination.…”

Section: Partial Least Squares Regression Model Developmentmentioning

confidence: 99%

“…They reported R 2 P values ranging from 0.86 to 0.89. Similarly, Posom et al (2022) [20] employed PLS regression to measure MC in cane bagasse, achieving an R 2 P value of 0.90. Sirisomboon et al (2020) [22] assessed MC in bamboo chips, obtaining an R 2 P value of 0.96, while Adnan et al (2017) [25] predicted the MC in intact green coffee beans, resulting in an R 2 P value of 0.96.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

“…Similarly, previous research studies have demonstrated the potential of utilizing NIR spectroscopy, as an alternative method, for assessing biomass proximate analysis parameters across the wavelength range between 780 and 2500 nm. Posom et al (2022) developed a reliable and accurate model for online measurement of MC in sugarcane [20]. Toscano et al (2022) estimated the MC of industrial wood chip fuel using a portable NIR spectrometer [21].…”

Section: Introductionmentioning

confidence: 99%

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NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

Shrestha,

Posom,

Sirisomboon

et al. 2024

Energies

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This study investigates the non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; ash content) in various chipped and ground biomass using a combination of destructive thermogravimetric analysis (TGA) and non-destructive near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). The thermogravimetric method determines proximate analysis data through TG and DTG curves, tracking biomass mass loss over time or temperature. NIRS scans chipped biomass in diffuse reflectance, and ground biomass in transflectance mode, covering the wavenumber range from 3595 to 12,489 cm−1. PLSR-based models (Full-PLSR, GA-PLSR, SPA-PLSR, MP PLSR 5-range method, and MP PLSR 3-range method) are developed and evaluated based on R2P, RMSEP, and RPD. MC and FC models for chip biomass exhibit satisfactory performance, making them cautiously applicable in various applications, including research. Optimal models for MC and FC in chip biomass, constructed using GA-PLSR with the second derivative and Full-PLSR with a constant offset, yield high R2P values (0.8654 and 0.8773), low RMSEP values (0.85% and 2.12%), and high RPD values (2.9 and 3.0), indicating applicative capabilities. Other parameters such as MC and FC in ground biomass, as well as VM and ash content in both chip and ground biomass, are found suitable for rough screening. Model sensitivity, assessed by calculating LOQ, indicates high sensitivity for VM in both chip and ground biomass and FC in chip biomass, as the calculated LOQ value is lower than the minimum reference values used during model development. However, for the remaining parameters, LOQ values surpass the established minimum reference value, suggesting limitations in predicting samples below the calibration range. Continuous model enhancement incorporating an ample number of representative biomass samples and consistent validation with unknown samples are imperative for ensuring accurate predictions.

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Section: Partial Least Squares Regression Model Developmentmentioning

confidence: 99%

Section: Comparison With Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

Shrestha,

Posom,

Sirisomboon

et al. 2024

Energies

Self Cite

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“…It has been shown to reliably predict MC in a variety of biomass fuels, such as forest residues, herbaceous plants, , and pelletized fuels . Some researchers used NIR spectroscopy to scan the NIR spectra of biomass fuels in motion to develop reliable models for the online prediction of biomass MC. , It was found that the prediction accuracy of biomass MC is related to the speed of movement of the fuel, as well as the scanning distance of the infrared spectrum. Various attempts have been made by researchers in NIR spectral data preprocessing and outlier detection …”

Section: Introductionmentioning

confidence: 99%

“…NIR spectroscopy methods that have indirect contact with the fuel can avoid this risk. It has been shown to reliably predict MC in a variety of biomass fuels, such as forest residues, herbaceous plants, , and pelletized fuels . Some researchers used NIR spectroscopy to scan the NIR spectra of biomass fuels in motion to develop reliable models for the online prediction of biomass MC. , It was found that the prediction accuracy of biomass MC is related to the speed of movement of the fuel, as well as the scanning distance of the infrared spectrum.…”

Section: Introductionmentioning

confidence: 99%

Moisture Prediction of Biomass Fuel Based on Near-Infrared Spectroscopy and Deep Learning Algorithm

Yan,

Dong,

Zhang

et al. 2024

Energy Fuels

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The moisture content of biomass (biomass MC) is related to the costs of power plants and affects the efficiency of the boiler. Online monitoring of the biomass MC will be beneficial for combustion control and biomass fuel management. In this study, the dimensions of biomass crushing and the height of the nearinfrared (NIR) spectrum acquisition on a power plant conveyor belt were simulated in an experimental setup. Multiple types of biomass samples from different regions were prepared under different moisture environments and scanned to obtain the NIR spectra of the biomass. Partial least squares (PLS), support vector regression (SVR), and backpropagation neural network (BPNN) were used to evaluate MC prediction models based on NIR data and deep feature data extracted by the deep autoencoder (DAE) and the supervised deep autoencoder (SDAE). The BPNN model performs best in modeling based on NIR data. The supervised deep autoencoder with the backpropagation fusion neural network (SDAE-BPFNN) model based on a deep learning algorithm achieved optimal performance in deep feature data modeling. The coefficient of the root-mean-square error of prediction (RMSEP) of SDAE-BPFNN was 2.51% wb, which is a relative reduction of 15.48% compared to the RMSEP of the BPNN based on NIR data. Nonlinear feature extraction of spectral data, retaining the information on variables related to MC, high accuracy, and strong robustness are the advantages of the proposed model based on deep learning algorithms.

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Comparison Between Linear and Nonlinear Machine-Learning Algorithms for Predicting the Properties of Biodiesel Using Near-infrared Spectra

Thongphut,

Chungcharoen,

Phetpan

2023

2023 5th International Conference on Control and Robotics (ICCR)

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Real-time measuring energy characteristics of cane bagasse using NIR spectroscopy

Cited by 5 publications

References 30 publications

NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

NIR Spectroscopy as an Alternative to Thermogravimetric Analyzer for Biomass Proximate Analysis: Comparison of Chip and Ground Biomass Models

Moisture Prediction of Biomass Fuel Based on Near-Infrared Spectroscopy and Deep Learning Algorithm

Comparison Between Linear and Nonlinear Machine-Learning Algorithms for Predicting the Properties of Biodiesel Using Near-infrared Spectra

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