Insights From Principal Component Analysis Applied to Py-GCMS Study of Indian Coals and Their Solvent Extracted Clean Coal Products

Abstract

The present work aims at studying five Indian coals and their solvent extracted clean coal products using Py-GCMS analysis and correlating these characterizations with results from theoretical a principal component analysis. The pyrolysis products of the original coals and the super clean coals were classified as mono-, di and tri- aromatics while other prominent products that were obtained included cycloalkanes, n-alkanes and alkenes ranging from C10-C29. The Py-GCMS results for the samples were studied using Principal Component Analysis. Inferences on relative composition of constituent compounds and functional groups and structural insights based on scores and loading plots of the PCA analysis were consistent with the experimental observations. ATR-FTIR studies confirmed the reduced concentration of ash in the super clean coals and the presence of aromatics. The Py-GCMS data and the ATR-FTIR spectra led to the conclusion that the super clean coals behaved similarly for both coking and non-coking coals with high aromatic concentrations as compared to the raw coal. Neyveli lignite super clean coal was found to show some structural similarity with the original coals, whereas, the other super clean coal showed structural similarity among them but not with their original coals indicative of the selective action of the e,N solvent system on the polycondensed aromatic structures in coal.

Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model

“…PCA simplification of influencing factors can effectively avoid the occurrence of this problem and can further reduce the dimension of influencing factors by obtaining new common factors[33][34][35].…”