Chakravarty, T
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."
1990
Modeling and Predicting the Composition of Fossil Fuel Derived Pyrolysis Liquids by Using Low-Voltage Mass Spectrometry and Canonical Correlation Analysis
Chakravarty, T.; Khan, M.R. and Meuzelaar, H.L.C.
Industrial and Engineering Research, 29 (11), 2173-2180, 1990. Funded by Consortium for Fossil Fuel Liquifaction Science.
Low-voltage electron ionization mass spectrometry (LV-EIMS) was performed on 25 fossil fuel samples (21 coals, 2 oil shades, 1 tar sand, and 1 coal resin concentrate) and their respective pyrolysis liquids prepared at Morgantown Energy Technology Center (METC) by means of a fixed-bed reactor. By using principal component analysis, the tar evaporation spectra and the solid fuel pyrolysis spectra were classified in terms of the underlying structural variables. In both data sets, all 4 non-coal samples, as well as 2 less typical coal samples, were found to be outliers. After removing the 6 outliers, canonical correlation analysis was performed on the remaining subsets of 19 coal samples in order to bring out the compositional similarities and differences between the fossil fuel samples and their pyrolysis liquids. By determining the common sources of variance between the two data sets by means of canonical correlation analyses, it was demonstrated that the canonical variate model enabled prediction of the mass spectrum of a given coal tar sample from the measured pyrolysis mass spectrum of the corresponding coal sample. Agreement with the experimental results was reasonably good.
1988-1986
Multivariate Analysis of Time-Resolved Mass Spectral Data
Windig, W.; Chakravarty, T.; Richards, J.M. and Meuzelaar, H.L.C.
Analytical Chemical Acta, 9, 205-2l8, 1986. 13 pgs. Funded by Army Research Office and Hercules.
Multivariate analysis of time-resolved pyrolysis/mass spectrometric data is described. The approach is based on the variance diagram (VARDIA), a recently developed technique that quantifies the clustering of variables in two-dimensional factor analysis (sub)spaces in a rotational scanning procedure. A maximum in the VARDIA plot indicates a correlated behavior of the mass variables, indicating a common origin. This common origin is generally caused by a change in the concentration of a chemical component. With this information the "factor spectrum" and the scores of the component can be retrieved. For time-resolved serial data, consideration of the clustering behavior of the variables as a function of time is more appropriate than a rotational scanning procedure. Adaptation of the VARDIA for serial data, such as time-resolved data, is described. This approach has the advantage that all the factors can be used. It will be shown that the resolution of the obtained curve can be higher than the total ion current curve as a function of time. Examples will be given for time-resolved data of coal, rubber and wood samples.
Time-Resolved Pyrolysis Mass Spectrometry of Coal: A New Tool for Mechanistic and Kinetic Studies
Chakravarty, T.; Meuzelaar, H.L.C.; Windig, W. and Hill, G.R.
Energy & Fuel, 2, 400-405, 1988. 5 pgs. Funded by ACERC (National Science Foundation and Associates and Affiliates).
Most coal devolatilization studies so far have focused on the determination of reaction rates for reactions occurring under widely different conditions encountered in liquefaction, gasification, coking or combustion processes. Published rates on more or less comparable coals may differ by several orders of magnitude, especially when obtained at high temperatures (>1000 K) and/or high heating rates (10²-105 K/s).
At the present state-of-the-art in coal devolatilization research, more emphasis should perhaps be placed on elucidating the mechanisms of the chemical reactions underlying the observed phenomena. When studying thermal conversion reactions in coal it seems correct to concentrate first on the so-called "primary" reactions before attempting to elucidate the many possible secondary reaction pathways. This is especially true since most secondary reaction pathways are strongly influenced by reactor design and experimental conditions.
The devolatilization behavior of coal will be determined primarily by the chemical composition of coal and secondly by the experimental conditions. Under properly designed vacuum micropyrolysis experiments working with sufficiently small particles (<50 mm diameter), it is possible to avoid mass and heat transport limitations and minimize the secondary reactions. Using unoxidized or well preserved coal samples, the chemical composition can be well defined and possibly characterized by major factors such as rank and depositional environment. Recent advances in pyrolysis mass spectrometry (Py-MS), viz, time-resolved Py-MS (TR Py-MS), along with multivariate analysis techniques enable extraction of underlying chemical components from a single experiment, thus reducing the uncertainty due to varying reactions conditions in different experiments. This paper demonstrates the feasibility of obtaining valuable mechanistic and kinetic data using microgram amounts of carefully selected coal samples under properly designed reaction conditions using TR Py-MS techniques in combination with advanced multivariate data analysis methods.
Prediction of the Composition of Coal Tars from the Pyrolysis Mass Spectra of the Parent Coals Using Canonical Correlation Techniques
Chakravarty, T.; Meuzelaar, H.L.C.; Jones, P.R. and Khan, M.R.
ACS Preprints, 33, (2), 235-241, 1988. Toronto, CA. Funded by ACERC (National Science Foundation and Associates and Affiliates).
Numerical comparison of compositional data on coals and their corresponding pyrolysis tars enables the construction of empirical mathematical models to predict liquid yield and composition from spectroscopic data of the parent coal. This approach was successful when using spectroscopic methods combined with vacuum micropyrolysis techniques, viz. Curie-point pyrolysis mass spectrometry. Nineteen US coals and the corresponding pyrolysis liquids prepared by the SHRODR method were analyzed by means of Curie-point pyrolysis low voltage MS. The pyrolysis mass spectra of the coals were composed of mainly primary pyrolysis products typical of vacuum micropyrolysis and were substantially different from the low voltage mass spectra of the corresponding SHRODR tars produced under batch autoclave conditions which promote the formation of secondary pyrolysis products. Nevertheless, it proved feasible to model and predict SHRODR tar spectra from the vacuum micropyrolysis spectra of the coals with a high degree of precision by means of factor analysis-based canonical correlation methods.
Chakravarty, T
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."
1990
Modeling and Predicting the Composition of Fossil Fuel Derived Pyrolysis Liquids by Using Low-Voltage Mass Spectrometry and Canonical Correlation Analysis
Chakravarty, T.; Khan, M.R. and Meuzelaar, H.L.C.
Industrial and Engineering Research, 29 (11), 2173-2180, 1990. Funded by Consortium for Fossil Fuel Liquifaction Science.
Low-voltage electron ionization mass spectrometry (LV-EIMS) was performed on 25 fossil fuel samples (21 coals, 2 oil shades, 1 tar sand, and 1 coal resin concentrate) and their respective pyrolysis liquids prepared at Morgantown Energy Technology Center (METC) by means of a fixed-bed reactor. By using principal component analysis, the tar evaporation spectra and the solid fuel pyrolysis spectra were classified in terms of the underlying structural variables. In both data sets, all 4 non-coal samples, as well as 2 less typical coal samples, were found to be outliers. After removing the 6 outliers, canonical correlation analysis was performed on the remaining subsets of 19 coal samples in order to bring out the compositional similarities and differences between the fossil fuel samples and their pyrolysis liquids. By determining the common sources of variance between the two data sets by means of canonical correlation analyses, it was demonstrated that the canonical variate model enabled prediction of the mass spectrum of a given coal tar sample from the measured pyrolysis mass spectrum of the corresponding coal sample. Agreement with the experimental results was reasonably good.
1988-1986
Multivariate Analysis of Time-Resolved Mass Spectral Data
Windig, W.; Chakravarty, T.; Richards, J.M. and Meuzelaar, H.L.C.
Analytical Chemical Acta, 9, 205-2l8, 1986. 13 pgs. Funded by Army Research Office and Hercules.
Multivariate analysis of time-resolved pyrolysis/mass spectrometric data is described. The approach is based on the variance diagram (VARDIA), a recently developed technique that quantifies the clustering of variables in two-dimensional factor analysis (sub)spaces in a rotational scanning procedure. A maximum in the VARDIA plot indicates a correlated behavior of the mass variables, indicating a common origin. This common origin is generally caused by a change in the concentration of a chemical component. With this information the "factor spectrum" and the scores of the component can be retrieved. For time-resolved serial data, consideration of the clustering behavior of the variables as a function of time is more appropriate than a rotational scanning procedure. Adaptation of the VARDIA for serial data, such as time-resolved data, is described. This approach has the advantage that all the factors can be used. It will be shown that the resolution of the obtained curve can be higher than the total ion current curve as a function of time. Examples will be given for time-resolved data of coal, rubber and wood samples.
Time-Resolved Pyrolysis Mass Spectrometry of Coal: A New Tool for Mechanistic and Kinetic Studies
Chakravarty, T.; Meuzelaar, H.L.C.; Windig, W. and Hill, G.R.
Energy & Fuel, 2, 400-405, 1988. 5 pgs. Funded by ACERC (National Science Foundation and Associates and Affiliates).
Most coal devolatilization studies so far have focused on the determination of reaction rates for reactions occurring under widely different conditions encountered in liquefaction, gasification, coking or combustion processes. Published rates on more or less comparable coals may differ by several orders of magnitude, especially when obtained at high temperatures (>1000 K) and/or high heating rates (10²-105 K/s).
At the present state-of-the-art in coal devolatilization research, more emphasis should perhaps be placed on elucidating the mechanisms of the chemical reactions underlying the observed phenomena. When studying thermal conversion reactions in coal it seems correct to concentrate first on the so-called "primary" reactions before attempting to elucidate the many possible secondary reaction pathways. This is especially true since most secondary reaction pathways are strongly influenced by reactor design and experimental conditions.
The devolatilization behavior of coal will be determined primarily by the chemical composition of coal and secondly by the experimental conditions. Under properly designed vacuum micropyrolysis experiments working with sufficiently small particles (<50 mm diameter), it is possible to avoid mass and heat transport limitations and minimize the secondary reactions. Using unoxidized or well preserved coal samples, the chemical composition can be well defined and possibly characterized by major factors such as rank and depositional environment. Recent advances in pyrolysis mass spectrometry (Py-MS), viz, time-resolved Py-MS (TR Py-MS), along with multivariate analysis techniques enable extraction of underlying chemical components from a single experiment, thus reducing the uncertainty due to varying reactions conditions in different experiments. This paper demonstrates the feasibility of obtaining valuable mechanistic and kinetic data using microgram amounts of carefully selected coal samples under properly designed reaction conditions using TR Py-MS techniques in combination with advanced multivariate data analysis methods.
Prediction of the Composition of Coal Tars from the Pyrolysis Mass Spectra of the Parent Coals Using Canonical Correlation Techniques
Chakravarty, T.; Meuzelaar, H.L.C.; Jones, P.R. and Khan, M.R.
ACS Preprints, 33, (2), 235-241, 1988. Toronto, CA. Funded by ACERC (National Science Foundation and Associates and Affiliates).
Numerical comparison of compositional data on coals and their corresponding pyrolysis tars enables the construction of empirical mathematical models to predict liquid yield and composition from spectroscopic data of the parent coal. This approach was successful when using spectroscopic methods combined with vacuum micropyrolysis techniques, viz. Curie-point pyrolysis mass spectrometry. Nineteen US coals and the corresponding pyrolysis liquids prepared by the SHRODR method were analyzed by means of Curie-point pyrolysis low voltage MS. The pyrolysis mass spectra of the coals were composed of mainly primary pyrolysis products typical of vacuum micropyrolysis and were substantially different from the low voltage mass spectra of the corresponding SHRODR tars produced under batch autoclave conditions which promote the formation of secondary pyrolysis products. Nevertheless, it proved feasible to model and predict SHRODR tar spectra from the vacuum micropyrolysis spectra of the coals with a high degree of precision by means of factor analysis-based canonical correlation methods.