Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods
Calorific value and moisture content are the key indices to evaluate <i>Caragana</i> pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. B...
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2025-07-01
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| author | Xuehong De Haoming Li Jianchao Zhang Nanding Li Huimeng Wan Yanhua Ma |
| author_facet | Xuehong De Haoming Li Jianchao Zhang Nanding Li Huimeng Wan Yanhua Ma |
| author_sort | Xuehong De |
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| description | Calorific value and moisture content are the key indices to evaluate <i>Caragana</i> pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in <i>Caragana korshinskii</i> fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of <i>Caragana korshinskii</i> fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>)</mo></mrow></semantics></math></inline-formula>, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula>, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. |
| format | Article |
| id | doaj-art-ebcee867cf2941d2be0dfd082ff9d703 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-ebcee867cf2941d2be0dfd082ff9d7032025-08-20T03:36:15ZengMDPI AGAgriculture2077-04722025-07-011514155710.3390/agriculture15141557Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric MethodsXuehong De0Haoming Li1Jianchao Zhang2Nanding Li3Huimeng Wan4Yanhua Ma5Faculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaFaculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaFaculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaFaculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaFaculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaFaculty of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010020, ChinaCalorific value and moisture content are the key indices to evaluate <i>Caragana</i> pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in <i>Caragana korshinskii</i> fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of <i>Caragana korshinskii</i> fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>)</mo></mrow></semantics></math></inline-formula>, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula>, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment.https://www.mdpi.com/2077-0472/15/14/1557hyperspectral imaging technologycharacteristic variable selectionbiomass fuel<i>Caragana korshinskii</i> |
| spellingShingle | Xuehong De Haoming Li Jianchao Zhang Nanding Li Huimeng Wan Yanhua Ma Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods Agriculture hyperspectral imaging technology characteristic variable selection biomass fuel <i>Caragana korshinskii</i> |
| title | Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods |
| title_full | Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods |
| title_fullStr | Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods |
| title_full_unstemmed | Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods |
| title_short | Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods |
| title_sort | prediction of the calorific value and moisture content of i caragana korshinskii i fuel using hyperspectral imaging technology and various stoichiometric methods |
| topic | hyperspectral imaging technology characteristic variable selection biomass fuel <i>Caragana korshinskii</i> |
| url | https://www.mdpi.com/2077-0472/15/14/1557 |
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