Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

Abstract

Machine learning algorithms are attracting significant interest for predicting complex chemical phenomenon. In this work, a model to predict research octane number (RON) and motor octane number (MON) of pure hydrocarbons, hydrocarbon-ethanol blends, and gasoline–ethanol blends has been developed using artificial neural networks (ANNs) and molecular parameters from 1H nuclear magnetic resonance (NMR) spectroscopy. RON and MON of 128 pure hydrocarbons, 123 hydrocarbon–ethanol blends of known composition, and 30 FACE (fuels for advanced combustion engines) gasoline–ethanol blends were utilized as a data set to develop the ANN model. The effect of weight percent of seven functional groups including paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic −CH═CH2 groups, naphthenic CH–CH2 groups, aromatic C–CH groups, and ethanolic OH groups on RON and MON was studied. The effect of branching (i.e., methyl substitution), denoted by a parameter termed as branching index (BI), and molecular w...