Prediction of the PIONA and oxygenate composition of unconventional fuels with the Pseudo-Component Property Estimation (PCPE) method. Application to an Automotive Shredder Residues-derived gasoline 2018-01-0905
To check if an unconventional fuel can be burned in an engine, monitoring the stability in terms of composition is mandatory. When the composition of a conventional fuel cannot be measured for practical reason, it can be approximated using the API (American Petroleum Institute) relations (Riazi-Daubert) linking the hydrocarbon group fractions with well-chosen properties. These relations cover only the paraffin (coupling iso and normal), naphthene and aromatic (PNA) groups as they were developed for conventional fuels presenting neglected amounts of olefins and oxygenates. Olefins and oxygenates can be present in unconventional fuels. This paper presents a methodology applicable to any unconventional fuel to build a model to estimate the n-paraffin, iso-paraffin, olefin, naphthene, aromatic and oxygenate (PIONAOx) composition. The current model was demonstrated for an automotive shredder residues (ASR)-derived gasoline-like fuel (GLF). The model was trained using real fractions measured with a comprehensive two-dimensional gas chromatography coupled with flame ionization detector (GC × GC-FID) technique. The lowest cumulated absolute error comparing with the confidence interval of the measured fractions was evaluated to be 12.4%. The model was tested for one fuel composition only, therefore, the error of the calculated fractions will be investigated with other fuels in future work.
Citation: Tipler, S., Parente, A., Coussement, A., Contino, F. et al., "Prediction of the PIONA and oxygenate composition of unconventional fuels with the Pseudo-Component Property Estimation (PCPE) method. Application to an Automotive Shredder Residues-derived gasoline," SAE Technical Paper 2018-01-0905, 2018, https://doi.org/10.4271/2018-01-0905. Download Citation
Steven Tipler, Alessandro Parente, Axel Coussement, Francesco Contino, Steffen H. Symoens, Marko R. Djokic, Kevin M. Van Geem
Université Libre de Bruxelles, Vrije Universiteit Brussel, Ghent University