Unraveling the complexity of pyrolysates from residual fuels by Py-GCxGC-FID/SCD/TOF-MS with an innovative data processing method

Abstract

Residual fuels, often called ’bottom-of-the-barrel’ are expected to play an important role in the transition towards a purely renewable energy market. Processes are required to upgrade such fuels and convert them into energy more cleanly and sustainably. To this goal, researchers aim to improve the understanding of their structure and reactivity. Given their complex nature and different chemical compositions, comprehensive two-dimensional gas chromatography (GCxGC) is well suited to provide a detailed characterization of the volatiles released during the pyrolysis of the residual fuels. This study reports a detailed characterization of pyrolysates released during the pyrolysis of two oil samples analyzed using a Py-GCxGC-FID/SCD/TOF-MS. The mass spectra generated with the electron impact ionization TOF-MS detection were used for species identification. An algorithm was developed and described here for the screening and post-processing of the detected peaks. Following algorithm post-processing, the species identification confirms that this workflow is suitable for unraveling the complex nature of the complex mixture released during the pyrolysis of the oil samples. The algorithm results were verified using information from a flame ionization detector (FID) and a sulfur chemiluminescence detector (SCD), and the extraction of fragmentation patterns based on the literature. The chemical structure of pyrolysis products is described and classified into 26 molecular classes. The methodology presented in this work can be extended to other complex mixtures, such as bio-oils, plastics, and biomasses.