Metcalf, GS
1992
Computer-Enhanced Pyrolysis Mass Spectrometry; A New Window on Coal Structure and Reactivity
Yun, Y.; Meuzelaar, H.L.C.; Chakravarty, T.; and Metcalf, G.S.
Chapter 12, Advances in Coal Sprectroscopy, (H.L.C. Meuzelaar, ed.), Plenum Publishing Corp., New York, 1992. [Previously published in Computer Enhanced Analytical Spectroscopy, Volume II, (Meuzelaar, H.L.C., ed.), Plenum Publishing Corp., New York, 1990]. Funded by Pittsburgh Energy Technology Center/CFFLS and ACERC.
Coals may be regarded as highly complex, fossilized assemblages of more or less strongly decomposed plant matter, microorganisms and humic substances in addition to a range of possible mineral constituents. Specific coal seams may represent peat-forming palaeoenvironments as diverse as river delta swamps, salt-water marshes or rain forest bogs, thus explaining the intrinsic heterogeneity of coal at the macroscopic as well as microscopic levels. Macroscopically, coal heterogeneity is often readily visible in the form of discrete bands representing successions of different depositional environments or, perhaps, catastrophic events such as floods and forest fires. At the microscopic level most coals display an even broader scale of diversity and heterogeneity in the form of microscopically distinct coal components generally referred to as "macerals."
1987
Characterization of US Lignites by Pyrolysis Mass Spectrometry and Multivariate Analysis
Metcalf, G.S.; Windig, W.; Gill, G.R. and Meuzelaar, H.L.C.
International Journal of Coal Geology, 7, 245-268, 1987. 23 pgs. Funded by US Department of Energy, Phillips Petroleum, and State of Utah.
Sixteen Texas (Gulf Province) lignite samples and six Montana and Wyoming (Northern Plains province) lignite samples were obtained from the Penn state coal sample collection and analyzed in triplicate by pyrolysis mass spectrometrey (Py-MS) using Curie-point pyrolysis (equilibrium temp. 610ºC) 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 Plains lignites) and an aliphatic (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 Plains and Gulf Province lignites 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 Penn State coal data bank on all twenty-two lignite 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 lignite samples from the two provinces. Subsequently, canonical variate analysis was used to rotate both the conventional and Py-MS data sets to a common set of vectors describing and 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 aliphatic vs. aromatic or hydrocarbon tendencies, as well as sulfur containing moieties. This enabled a final, tentative synthesis of all the lignite data into several highly simplified schemes relating compositional aspects to depositional environments.
Metcalf, GS
1992
Computer-Enhanced Pyrolysis Mass Spectrometry; A New Window on Coal Structure and Reactivity
Yun, Y.; Meuzelaar, H.L.C.; Chakravarty, T.; and Metcalf, G.S.
Chapter 12, Advances in Coal Sprectroscopy, (H.L.C. Meuzelaar, ed.), Plenum Publishing Corp., New York, 1992. [Previously published in Computer Enhanced Analytical Spectroscopy, Volume II, (Meuzelaar, H.L.C., ed.), Plenum Publishing Corp., New York, 1990]. Funded by Pittsburgh Energy Technology Center/CFFLS and ACERC.
Coals may be regarded as highly complex, fossilized assemblages of more or less strongly decomposed plant matter, microorganisms and humic substances in addition to a range of possible mineral constituents. Specific coal seams may represent peat-forming palaeoenvironments as diverse as river delta swamps, salt-water marshes or rain forest bogs, thus explaining the intrinsic heterogeneity of coal at the macroscopic as well as microscopic levels. Macroscopically, coal heterogeneity is often readily visible in the form of discrete bands representing successions of different depositional environments or, perhaps, catastrophic events such as floods and forest fires. At the microscopic level most coals display an even broader scale of diversity and heterogeneity in the form of microscopically distinct coal components generally referred to as "macerals."
1987
Characterization of US Lignites by Pyrolysis Mass Spectrometry and Multivariate Analysis
Metcalf, G.S.; Windig, W.; Gill, G.R. and Meuzelaar, H.L.C.
International Journal of Coal Geology, 7, 245-268, 1987. 23 pgs. Funded by US Department of Energy, Phillips Petroleum, and State of Utah.
Sixteen Texas (Gulf Province) lignite samples and six Montana and Wyoming (Northern Plains province) lignite samples were obtained from the Penn state coal sample collection and analyzed in triplicate by pyrolysis mass spectrometrey (Py-MS) using Curie-point pyrolysis (equilibrium temp. 610ºC) 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 Plains lignites) and an aliphatic (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 Plains and Gulf Province lignites 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 Penn State coal data bank on all twenty-two lignite 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 lignite samples from the two provinces. Subsequently, canonical variate analysis was used to rotate both the conventional and Py-MS data sets to a common set of vectors describing and 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 aliphatic vs. aromatic or hydrocarbon tendencies, as well as sulfur containing moieties. This enabled a final, tentative synthesis of all the lignite data into several highly simplified schemes relating compositional aspects to depositional environments.