Characterization of U.S. lignites by pyrolysis mass spectrometry and multivariate analysis

Abstract

Sixteen Texas (Gulf Province) lignite samples and six Montana and Wyoming (Northern Great Plains Province) lignitic coals were obtained from the Pennsylvania State University Coal Bank and analyzed in triplicate by pyrolysis mass spectrometry (Py-MS) using Curie-point pyrolysis (equilibrium temp. 610°C) in combination with low-voltage (12 eV) electron ionization. The spectra obtained were evaluated by means of factor analysis, followed by discriminant analysis using only factors with eigenvalue ⩾ 1 and regarding each set of triplicate spectra as a separate category. The discriminant analysis results showed a definite separation between lignites from the two provinces as well as some clustering of samples from the same seam field or region. Six additional lignite samples obtained from an independent source and representing other regions of the Gulf Province were found to cluster with the Texas lignite samples when treated as “unknowns” in the discriminant analysis procedure. Chemical interpretation of the spectral differences underlying the clustering behavior of the lignite samples in the discriminant analysis procedure was attempted using a newly developed, unsupervised numerical extraction method for chemical components in complex spectra. This procedure, the Variance Diagram (VARDIA) technique, revealed the presence of six major chemical component axes. Examination of the spectral patterns corresponding to these component axes showed a softwood lignin-like component (high in Northern Great Plains lignitic coals) and an alipathic (algal?) hydrocarbon component (high in Gulf lignites) to be primarily responsible for the differences between the two provinces. In addition, two biomarker patterns, namely a terpenoid resin-like component and an unknown component, were shown to be highly characteristic for the Northern Great Plains and Gulf Province samples, respectively. Two other component axes were found to consist largely of sulfur-containing ion series, one of which appeared to represent an obvious marine influence on the South Texas region of the Gulf Province. Furthermore, a set of seventeen conventional coal parameters, including petrographic, ultimate and proximate analysis data as well as sulfur content, calorific value and vitrinite reflectance, obtained from the Pennsylvania State University coal data bank on all twenty-two samples, was also submitted to factor analysis. Comparison of the scores of the first two factors from this set with the scores of the first two discriminant functions of the Py-MS data set revealed an overall similarity in clustering behavior of the samples from the two provinces. Subsequently, canomical variate analysis was used to rotate both the conventional and Py-MS data sets to a common set of vectors describing the correlating (“overlapping”) portions of both data sets. Examination of the first two pairs of canonical variate functions revealed strong correlations between the conventional data and the Py-MS data, e.g., with regard to alipathic vs. aromatic or hydrocarbon vs. heteroatomic tendencies, as well as sulfur containing moieties. This enabled a final, tentative synthesis of all the data into several highly simplified schemes relating compositional aspects to depositional environments.