Determination of the carbon content in coal and ash by XRF

Parus, J.; Kierzek, J.; Małżewska-Bućko, B.

doi:10.1002/(sici)1097-4539(200003/04)29:2<192::aid-xrs421>3.0.co;2-t

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…The K line of carbon can be measured both with high-end energy-dispersive XRF and with wavelength-dispersive XRF, but only if carbon is present in relatively high concentrations (e.g. above 50%) (Parus et al, 2000). Moreover, below such a threshold, the measured intensity of the emission line of the heavy atom becomes independent of its weight fraction, making the analysis impossible (Grieken & Markowicz, 2001) unless the sample is diluted with a lighter element.…”

Section: The Potentialities and Limitations Of Xrpd And Xrfmentioning

confidence: 99%

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Lopresti¹,

Mangolini²,

Milanesio³

et al. 2022

J Appl Crystallogr

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In materials and earth science, but also in chemistry, pharmaceutics and engineering, the quantification of elements and crystal phases in solid samples is often essential for a full characterization of materials. The most frequently used techniques for this purpose are X-ray fluorescence (XRF) for elemental analysis and X-ray powder diffraction (XRPD) for phase analysis. In both methods, relations between signal and quantity do exist but they are expressed in terms of complex equations including many parameters related to both sample and instruments, and the dependence on the active element or phase amounts to be determined is convoluted among those parameters. Often real-life samples hold relations not suitable for a direct quantification and, therefore, estimations based only on the values of the relative intensities are affected by large errors. Preferred orientation (PO) and microabsorption (MA) in XRPD cannot usually be avoided, and traditional corrections in Rietveld refinement, such as the Brindley MA correction, are not able, in general, to restore the correct phase quantification. In this work, a multivariate approach, where principal component analysis is exploited alone or combined with regression methods, is used on XRPD profiles collected on ad hoc designed mixtures to face and overcome the typical problems of traditional approaches. Moreover, the partial or no known crystal structure (PONKCS) method was tested on XRPD data, as an example of a hybrid approach between Rietveld and multivariate approaches, to correct for the MA effect. Particular attention is given to the comparison and selection of both method and pre-process, the two key steps for good performance when applying multivariate methods to obtain reliable quantitative estimations from XRPD data, especially when MA and PO are present. A similar approach was tested on XRF data to deal with matrix effects and compared with the more classical fundamental-parameter approach. Finally, useful indications to overcome the difficulties of the general user in managing the parameters for a successful application of multivariate approaches for XRPD and XRF data analysis are given.

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Section: The Potentialities and Limitations Of Xrpd And Xrfmentioning

confidence: 99%

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Lopresti¹,

Mangolini²,

Milanesio³

et al. 2022

J Appl Crystallogr

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“…Common rapid detection techniques include instantaneous gamma neutron activation analysis, X-ray fluorescence technology, inductively coupled plasma emission spectrometry, and so on. However, these techniques have a series of intractable problems, such as radiation hazards [1], limited analytical range [2], and large amount of argon gas consumption [3]. In this context, laser-induced breakdown spectroscopy (LIBS) technology emerged as an emerging rapid detection technology, which has significant advantages such as real-time detection and no need for sample pretreatment, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Semi-Supervised Learning Model to Coal Sample Classification

Wang,

Xu,

Gao

et al. 2024

Applied Sciences

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As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.

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“…In fact, the "smart boiler" can achieve real-time optimization according to the coal information; thus, a rapid detection method is urged to be applied in coal-fired power plants for the improvement of combustion efficiency and pollution reduction. Although there are some existing rapid detection methods for coal quality, these methods have some limitations for the application to remove measurement due to high installation and maintenance costs and strict safety supervision, for example, prompt gamma-ray neutron activation analysis (PGNAA) has potential radiation hazards [1]; X-ray fluorescence (XRF) cannot analyze low atomic number elements, such as C and H [2]; and inductively coupled plasma emission spectrometer (ICP-OES) needs to consume a large amount of argon [3]. However, LIBS has great advantages in the application of the rapid detection of coal with simple sample preparation and multi-element simultaneous measurement [4].…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification and Quantification of Coal by Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Zheng¹,

Lu²,

Chen

et al. 2023

Applied Sciences

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Coal is expected to be an important energy resource for some developing countries in the coming decades; thus, the rapid classification and qualification of coal quality has an important impact on the improvement in industrial production and the reduction in pollution emissions. The traditional methods for the proximate analysis of coal are time consuming and labor intensive, whose results will lag in the combustion condition of coal-fired boilers. However, laser-induced breakdown spectroscopy (LIBS) assisted with machine learning can meet the requirements of rapid detection and multi-element analysis of coal quality. In this work, 100 coal samples from 11 origins were divided into training, test, and prediction sets, and some clustering models, classification models, and regression models were established for the performance analysis in different application scenarios. Among them, clustering models can cluster coal samples into several clusterings only by coal spectra; classification models can classify coal with labels into different categories; and the regression model can give quantitative prediction results for proximate analysis indicators. Cross-validation was used to evaluate the model performance, which helped to select the optimal parameters for each model. The results showed that K-means clustering could effectively divide coal samples into four clusters that were similar within the class but different between classes; naive Bayesian classification can distinguish coal samples into different origins according to the probability distribution function, and its prediction accuracy could reach 0.967; and partial least squares regression can reduce the influence of multivariate collinearity on prediction, whose root mean square error of prediction for ash, volatile matter, and fixed carbon are 1.012%, 0.878%, and 1.409%, respectively. In this work, the built model provided a reference for the selection of machine learning methods for LIBS when applied to classification and qualification.

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Determination of the carbon content in coal and ash by XRF

Cited by 15 publications

References 6 publications

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Application of Semi-Supervised Learning Model to Coal Sample Classification

Rapid Classification and Quantification of Coal by Using Laser-Induced Breakdown Spectroscopy and Machine Learning

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