Modeling study of the anti-knock tendency of substituted phenols as additives: an application of the reaction mechanism generator (RMG)

Abstract

This work presents kinetic modeling efforts to evaluate the anti-knock tendency of several substituted phenols if used as gasoline additives. They are p-cresol, m-cresol, o-cresol, 2,4-xylenol, 2-ethylphenol, and guaiacol. A detailed kinetic model was constructed to predict the ignition of blends of the phenols in n-butane with the help of reaction mechanism generator (RMG), an open-source software package. The resulting model, which has 1465 species and 27 428 reactions, was validated against literature n-butane ignition data in the low-to-intermediate temperature range. To rank the anti-knock tendency of the additives, engine-like simulations were performed in a closed adiabatic homogenous batch reactor with a volume history derived from the pressure profile of a real research octane number (RON) engine test. The ignition timings of the additive blends were compared to that of primary reference fuels (PRFs) to quantitatively predict the anti-knock ability. The model predictions agree well with experimental determinations of the changes in RON induced by the additives. This study explains the chemical mechanism by which methyl-substituted phenols increase RON, and demonstrates how fundamental chemical kinetics can be used to evaluate practical fuel additive performance.

Detailed kinetics of substituted phenolic species in pyrolysis bio-oils

“…40 Huang et al 41,42 and Liu et al 43,44 also investigated some important reaction pathways in guaiacol pyrolysis based on B3LYP/6-31G++ (d,p) calculations, also explaining the formation of catechol, a relevant precursor of polycyclicaromatic hydrocarbon (PAH) components. Very recently Zhang et al 45 investigated the anti-knock potential of several substituted phenols, including guaiacol, further highlighting the current interest in this class of compounds for combustion applications, even beyond bio-oils.…”

Moving from postdictive to predictive kinetics in reaction engineering

“…The detailed‐model approach, with key parameters computed using quantum chemistry, has been able to predict the chemistry of several reaction systems even before experimental data were available 101,102 . In these cases, the conventional postdictive approach would be impossible, since there were no experimental data available to use to determine the model parameters.…”

“…Zhang et al 101 studied one class of biomass‐derived molecules, substituted phenols derived from biomass pyrolysis. There are many different phenols which could be made this way; the key unknown question at the time of the study was which phenols were most effective at increasing octane.…”

“…The correlation between the predicted changes in ignition delays and the experimental ΔRON numbers are shown in Figure 3. The scales are chosen to be representative of the relationship between the two quantities 101 . The experimental RON measurements have uncertainties of about 0.5 of a RON unit.…”

Scalability strategies for automated reaction mechanism generation

“…2 Automated reaction mechanism generation 2.1 RMG -Overall structure and key components This section outlines the overall structure and the key components that govern the computational performance of RMG simulations. Details regarding the chemical meaningfulness and accuracy of the employed methods have been published previously Hansen et al, 2013;Zhang et al, 2018;Gudiyella et al, 2018). RMG uses the rate-based algorithm of Susnow et al (1997) to iteratively grow ("enlarge") the reaction mechanism one or more species at a time.…”