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It is difficult to obtain comprehensive information on coal pyrolysis with a single analytical tool due to the diversity of coal pyrolysis products. Low-temperature pyrolysis of coals with three ranks was characterized by online analytical methods to accurately explore pyrolysis mechanisms. Thermogravimetric (TG) analysis was used to characterize the basic pyrolysis characteristics of the coals. In-situ pyrolysis technique combined with gas chromatography-mass spectrometry (Py-GC/MS) and pyrolysis synchrotron radiation vacuum ultraviolet photoionization time-of-flight mass spectrometry (Py-SVUV-PI-TOF-MS) were employed in explaining the mechanism of coal pyrolysis at low temperature. The temperature of macromolecules fragmentation increases with coal rank, and a higher coal rank leads to aromatic products with a larger ring size. Different trends of adsorbed substances in coal pores with coal rank and the corresponding pyrolysis products were analyzed by Py-SVUV-PI-TOF-MS. The peak area of ethanol decreased with increasing temperature, while the pyrolysis products increased. By combining Py-GC/MS and Py-SVUV-PI-TOF-MS, possible coal macromolecular structures were obtained by reverse extrapolation of the pyrolysis products. • Two on-line MS techniques were used to characterize pyrolysis products of coals. • The fracture temperature of the coal structure is obtained by Py-GC/MS. • Adsorbed gases and pyrolysis products are distinguished by Py-SVUV-PI-TOF-MS. [ABSTRACT FROM AUTHOR]

Catalytic deoxygenation of coal is an important step to obtain high quality coal-derived liquid fuels. Bimetallic catalysts exhibit excellent hydrodeoxygenation performance, because their synergistic effects. In this work, NiO–Mo 2 O 3 /ZSM-5 was prepared by impregnation method, and applicated to catalytic hydrodeoxygenation of Wucaiwan long flame coal (WCW). The thermal dissolution (TD) and catalytic hydrodeoxygenation (HDO) of WCW were investigated using cyclohexane as the solvent. The soluble portions from TD (SF NHDO) and HDO (SF HDO) were analyzed by gas chromatograph/mass spectrometry. The relative abundance of arenes and alkanes in SF HDO increased by 12.30 % and 29.98 %, respectively, compared to that in SF NHDO. Most detected oxygen-containing compounds in SF NHDO were alcohols and ethers, and their relative abundances decreased by 50.27 % and 84.13 %, respectively, after the catalytic treatment. Thus, the cleavage of C–O bond and deoxygenation in WCW were significantly proceeded during the HDO of coal over NiO–Mo 2 O 3 /ZSM-5 catalyst. The hydrodeoxygenation mechanism was proposed according to the catalytic deoxygenation of model compounds. Mobile H+ attacks oxygen atom and iso-carbon in coal, and produces aryl-molecules and aromatic alcohols. Aryl-molecules can be also generated from the attacking of H+ to the oxygen atom in aromatic alcohols. • NiO–Mo 2 O 3 /ZSM-5 was prepared for hydrodeoxygenation of long flame coal. • The catalyst is effective for the generation of arenes and alkanes. • Hydrodeoxygenation mechanism was revealed via catalytic reaction of model compounds. [ABSTRACT FROM AUTHOR]

• Functional groups are effective variables of machine learning for FTIR spectra. • OH is a key factor discriminating the samples because of deoxidation reactions. • Pre-integration of initial samples increases the classification effects of PCA. Three biomass (rice straw, rice husk and wheat straw) and three coal samples (lignite, subbituminous coal and bituminous coal) were extracted via a thermal dissolution (TD) process, and the TD extracts were treated by catalytic hydrogenation to obtain reaction products. To reveal the similarity in molecular information among the samples and elucidate their chemical reactivity, four machine learning algorithms were applied to analyze the Fourier transform infrared spectra of both TD extracts and catalytic hydrogenation products. Functional groups were used as variables and the difference in peak area can be treated as the basis for sample classification. Aromatic C H, C O C, aliphatic CH 2 or CH 3 and aromatic C O or C C bonds were the main characteristic variables in principal component analysis algorithm to classify biomass- and coal-derivated samples. These samples were also grouped into four clusters by hierarchical clustering analysis algorithm according to the similarity and difference in the distribution of functional groups. For artificial neural network algorithm, aliphatic C H and OH bonds are the most important variables to classify these samples into four groups, and aromatic C H, OH, and C O C groups are the main variables contributed to the classification trees in random forest algorithm. Machine learning algorithms will provide methodological guidance for the data mining of the spectra of complex organic systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • Spectral stitching improves the detection ability towards minor and trace species. • Comprehensive molecular information and transfer behavior of O and N atoms are revealed. • Similarity and difference of samples are visually exhibited in PCA and HCA plots. The combination of mass spectrometry (MS) data and chemometrics methods is conducive to obtaining more comprehensive molecular information and evaluating the transfer behavior of O and N atoms during the conversion of biomass and coal. In this work, 12 thermal dissolution (TD) extracts of coal/biomass samples and the catalytic hydrogenation products of TD extracts were analyzed by Orbitrap MS using spectral stitching and in-source collision-activated dissociation (ISCAD) methods. Spectral stitching improves the detection ability towards minor and trace species, enhancing the number of identified compounds by two times. Meanwhile, ISCAD provides more comprehensive molecular structure information, which can illustrate the transfer patterns of heteroatoms and deepen the understanding of the structural characteristics of aromatic rings during the conversion of biomass and coal. Two chemometrics methods, principal component analysis (PCA) and hierarchical cluster analysis (HCA), were introduced to dig out the detailed similarities and differences between O-containing and N-containing compounds in coal and biomass. HCA method not only recognized the category of O-containing and N-containing compounds in each sample, but also showed the removal behavior of O and N atoms in biomass and coal. Thus, the combination of MS methods and chemometrics method is expected to provide methodological support to elucidate the molecular structure and conversion pathways for complex coal and biomass. [ABSTRACT FROM AUTHOR]