Predicting physical properties of oxygenated gasoline and diesel range fuels using machine learning

Understanding the physical properties of distillate petroleum fuels like gasoline and diesel is very critical to ensure the normal operation of internal combustion (IC) engines with regards to processes like spray atomization, heating, evaporation etc. Two of most important physical properties are density and viscosity. Many factors such as molecular structure, molecular weight, temperature etc. effect the physical properties of the fuel. The present work deals with the development of a machine learning model for predicting the density and viscosity of petroleum fuels containing oxygenated chemical classes such as alcohols, esters, ketones and aldehydes. The model was developed using the molecular structure of the compounds expressed in the form of functional groups as inputs. The density and viscosity of 164 pure compounds spanning various chemical families and 14 blends of known compositions was collected from the literature. An artificial neural network model (ANN) for predicting density and viscosity was developed using the neural network tool in Matlab. Each of the ANN model was tested against 15% of the data and the results show that the models were able to successfully predict the density and viscosity of the unseen data points to a good accuracy. A regression coefficient of 0.99 (for density) and 0.98 (for viscosity) was obtained for the test set. The developed models can be used to predict and screen the density and viscosity of real petroleum fuels containing drop in oxygenated bio-fuels.& COPY; 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

Understanding the physical properties of distillate petroleum fuels like gasoline and diesel, such as density and viscosity, is crucial for the normal operation of internal combustion engines. This study developed a machine learning model using molecular structure as inputs to predict the density and viscosity of petroleum fuels containing oxygenated chemical classes. The model showed good accuracy in predicting unseen data points, with a regression coefficient of 0.99 for density and 0.98 for viscosity.