Application of chemometric tools for coal classification and multivariate calibration by transmission and drift mid-infrared spectroscopy.

The aim of this paper focuses on the determination of nine coal properties related to combustion power plants (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal kg(-1)), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) by mid-infrared spectroscopy. For that, a wide and diverse coal sample set has been clustered into new homogeneous coal subgroups by the use of hierarchical clustering analysis. This process was performed including property values and spectral data (scores of principal component analysis, PCA) as independent variables. Once the clusters were defined, the corresponding property calibration models were performed by partial least squares regression. Several mathematical pre-treatments were applied to the original spectral data in order to cope with some non-linearities. The accuracy and precision levels for each property were studied. The results revealed that coal properties related to organic components presented relative error values around 2% for some clusters, comparable to those provided by commercial online analysers. Finally, the discrimination level between those groups of samples was evaluated by linear discriminant analysis (LDA). The sensitivity of the system was studied accomplishing percentages close to 100% when the samples were classified attending only to their mid-infrared spectra.

Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis.

In the present paper, the influence of different acquisition techniques (transmission, diffuse reflectance infrared Fourier transform and attenuated total reflectance) in the determination of nine coal properties related to combustion power plants has been studied. Raw coal samples of different origins were pooled for developing a correlation between the resultant spectra and the corresponding coal properties by multivariate analysis techniques. Thus, the existent collinearity in mid-infrared coal spectra led to the application of partial least squares regression (PLS), studying simultaneously the influence of different spectroscopic units as well as several spectral data mathematical pre-treatments. On the other hand, a principal component analysis (PCA) revealed a relationship between principal components and coal composition in both transmission and reflection techniques. Although the best accuracy and precision results were obtained for coal properties related to organic matter, the system was also able to differentiate coal samples attending to the presence of a specific mineral matter, kaolinite.

Preparation and characterization of carbon-enriched coal fly ash.

Carbon-enriched fractions have been obtained from two coal fly ash (FA) samples. The FA came from two pulverized-coal fired power stations (Lada and Escucha, Spain) and were collected from baghouse filters. Sieving was used to obtain carbon-enriched fractions, which were further subjected to two beneficiation processes: acid demineralization using HCl and HF, and oil agglomeration using soya oil-water. Yield in weight after sieving, unburned carbon content, and several physicochemical characteristics of the obtained fractions were used to compare the performance of the beneficiation methods. Low carbon concentration was obtained by sieving, particularly in the case of Escucha FA. However, after acid demineralization or oil agglomeration, fractions containing unburned carbon in a range of 63% to 68% were obtained. These fractions showed differences in mineral phase composition and distribution depending on the FA and on the beneficiation method used. The textural properties of the obtained fractions varied as a function of their carbon content and the beneficiation method used. However, no significant differences in morphology of the carbonaceous particles were found.

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis.

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.