Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

Pitak, Lakkana; Sirisomboon, Panmanas; Saengprachatanarug, Khwantri; Wongpichet, Seree; Posom, Jetsada

doi:10.1016/j.energy.2020.119698

Cited by 13 publications

(10 citation statements)

References 49 publications

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“…Pitak et al investigated the possibility of determining C, H, and N via line‐scan near‐infrared (NIR) hyperspectral image technology 100, whereas Mata‐Sanchez et al used NIR to detect Cl, S, and K 101. Furthermore, FTIR coupled with partial least‐squares regression is used to determine the chemical elements like K, Ca, Mg, S, and Si in the study of Edmunds et al 102.…”

Section: Discussionmentioning

confidence: 99%

“…Different studies have investigated the usability of different IR spectroscopy methods and have determined different elemental compositions in solid biofuels. A study by Pitak et al investigated the possibility of determining C, H, and N via line‐scan near‐infrared (NIR) hyperspectral image technology 100. Mata‐Sanchez et al used NIR to detect Cl, S, and K 101.…”

Section: Analysis Of Solid Biofuelsmentioning

confidence: 99%

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Analytical Methods for the Rapid Determination of Solid Biofuel Quality

Endriss

Kuptz

Hartmann

et al. 2023

Chemie Ingenieur Technik

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Fuel properties of solid biofuels are essential aspects for the energy‐efficient and low‐emission operation of biomass heat and power plants. Hence, fuel quality parameters are often defined and used for pricing in supply contracts. To simplify and accelerate analytical approaches, rapid analysis devices are required to determine fuel properties such as water‐ and ash content, calorific value, and chemical composition on‐site. This article gives an overview about available technologies and, if applicable, their current state of use as rapid analysis devices for solid biofuels.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis Of Solid Biofuelsmentioning

confidence: 99%

Analytical Methods for the Rapid Determination of Solid Biofuel Quality

Endriss

Kuptz

Hartmann

et al. 2023

Chemie Ingenieur Technik

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“…Both GA and SPA were employed to select concise and influential wavenumbers, aiming to prevent overfitting and result in an improved prediction model [27]. GA, inspired by Charles Darwin's theory of natural selection, utilizes an optimization technique that generates a population of potential solutions and evolves them over multiple generations through selection, crossover, and mutation.…”

Section: Spectral Preprocessing and Model Developmentmentioning

confidence: 99%

“…There were two reports of our group contributed to the research results of NIR prediction models for ultimate analysis parameters of the non-wood and wood samples, including Pitak et al [27] who developed the PLS regression using the spectra obtained by line-scan NIR hyperspectral imager in which the most effective model for the prediction of C, H, and N content of 160 non-wood and wood biomass pellets including filter cake (15 pellets), Leucaena leucocepphala (10 pellets), bamboo (15 pellets), cassava rhizome (15 pellets), bagasse (15 pellets), sugarcane leaves (15 pellets), straw (15 pellets), rice husk (15 pellets), eucalyptus bark (15 pellets), napier grass (15 pellets), and corn cob (15 pellets) developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with R 2…”

Section: Introductionmentioning

confidence: 99%

Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy

Shrestha,

Posom,

Sirisomboon

et al. 2024

Energies

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The ultimate analysis parameters, including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass, were rarely found to be predicted by non-destructive tests to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near-infrared (NIR) raw spectra of non-wood and wood samples from fast-growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5-range, and MP 3-range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples were non-wood samples of 65–67% and wood samples of 33–35%, and the model performance was evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA–PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR–5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR-3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of the H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in the calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for a robust model, but also must provide their trend line characteristics in the scatter plot, i.e., correlation coefficient (R), slope, and intercept (same slope and slope approached to 1, and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope, and intercept to obtain the better-optimized model was studied. The results show that the different species affected the model performance of each parameter prediction in a different manner, and by scatter plot analysis, which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time the effect has been studied by the principle of a scatter plot.

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“…It has been noted that NIR spectroscopy can be applied to assess the composition of analytes. This is an indirect method of measurement, and its accuracy cannot be equal to that of the laboratory method [18,19]. The application of NIR is acceptable if the error does not exceed the control limit.…”

Section: Introductionmentioning

confidence: 99%

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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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

Cited by 13 publications

References 49 publications

Analytical Methods for the Rapid Determination of Solid Biofuel Quality

Analytical Methods for the Rapid Determination of Solid Biofuel Quality

Effect of Combined Non-Wood and Wood Spectra of Biomass Chips on Rapid Prediction of Ultimate Analysis Parameters Using near Infrared Spectroscopy

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

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