Machine learning based predictive models are being&nbsp;extensively applied for predicting combustion properties like cetane number (CN), which is the measure of a fuels ignition quality.&nbsp;In the present work, a comprehensive model was developed using artificial neural networks (ANN) that can predict the ignition quality&nbsp;of fuels containing a large number of chemical classes, namely parraffins, iso-paraffins, olefins, naphthenes, aromatics, alcohols, ethers, ketones, esters and carboxylic acid. Experimental CN's of&nbsp;555 fuels was used as a dataset for devolping the CN model. The compositional data of the fuels in the dataset, expressed in the form of&nbsp;eleven functional groups along with&nbsp;branching index (BI) and molecular&nbsp;weight were used as the input parameters of&nbsp;the model. These input parameters for the transportation fuels in the dataset were measured&nbsp;from 1H nuclear magnetic resonance (NMR) spectra, whereas the input parameters for pure components (and their blends)&nbsp;were calculated from their molecular structure. A shallow neural network with a single hidden layer was developed using Bayesian regularization. The developed model was validated with 15 % of the data points that were randomly generated and kept aside for model validation. A&nbsp;regression coefficient (R2) of 0.98&nbsp;was observed between the predicted&nbsp; and the experimental&nbsp;values along with an average absolute error of 1.1. The results showed that the developed model was&nbsp;succesful in predicting the CN of fuels&nbsp;and can be applied to pure compounds, blends and real fuels containing varied chemical functionalities.&nbsp;

A Comprehensive Model for Cetane Number Prediction Using Machine Learning

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

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