Application of Multivariate Linear Regression for Determination of Ash Content in Coal by XRF Analysis

Wawrzonek, L.; Parus, J.

doi:10.1080/10256018808623904

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…23 XRF has been widely used in coal analysis due to its fast measurement speed and good stability. For example, Wawrzonek 24 et al used multiple linear regression to confirm that the intensity of K α lines of Si and Ca in XRF spectrum and the backscattering intensity have the best correlation with ash content. Tiwari 25 et al analyzed Na, K, Si, Al, Ca, Mg, Zn, Fe, Mn and other elements in coal, fly ash and slag of various coal-fired power plants in India and optimized the voltage and current of the X-ray tube.…”

Section: Introductionmentioning

confidence: 99%

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

Gao

Wang

et al. 2023

Anal. Methods

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Section: Introductionmentioning

confidence: 99%

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

Gao

Wang

et al. 2023

Anal. Methods

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“…Besides, the power of X-ray can be much lower for radiation safety. Wawrzonek and Parus tried multivariate linear regression with elemental lines of XRF to determine the ash content in coal [18]. Ma utilized the EDXRF instrument to evaluate the measurements of Si, Al, Fe, Ca, Mg, Ti, K, Na, and P contents in coal ash [19].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling

Bai

Zhang³

et al. 2022

Front. Phys.

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The combination of laser-induced breakdown spectroscopy and energy dispersive X-ray fluorescence spectroscopy in the coal quality analysis was reported formerly. But in the practical test of the prototype instrument in the real power plant, the X-ray fluorescence signals suffered from intensity fluctuations over long-time measurements. The long-term signal fluctuations cause lower efficiency on the establishment of the calibration model and relatively larger root-mean-squared error of prediction (RMSEP) for unknown samples. Therefore, the spectral intensity correction was performed in the measurements; a randomly selected sample was measured several times in the whole measurements, including the modeling samples and unknown samples, recording the signal fluctuations and searching for a set of factors suitable for the intensity correction of a full-spectrum–based partial least square calibration model. In addition, as the signals of the coal samples of the power plant showed the potential of classification, the piecewise models were also established in case of further enhancement of the model or prediction accuracy. The RMSEPs of the calorific value, ash, volatile, and sulfur were lowered from 0.68 MJ/kg, 1.62%, 0.32%, and 0.24% to 0.51 MJ/kg, 1.34%, 0.16%, and 0.14% after spectral intensity correction, respectively. The piecewise modeling with spectral intensity correction achieved similar RMSEP for volatile and sulfur prediction but with more accurate models. The spectral intensity correction showed the ability to reduce the long-term signal fluctuation, and piecewise modeling also showed more efficiency in the model establishments for volatile and ash determination.

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Analysis by absorption and scattering of radiation a current bibliography

Bujdosó

1990

Journal of Radioanalytical and Nuclear Chemistry, Articles

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Application of Multivariate Linear Regression for Determination of Ash Content in Coal by XRF Analysis

Cited by 5 publications

References 3 publications

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling

Analysis by absorption and scattering of radiation a current bibliography

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