Octane number prediction and optimization for gasoline blends using artificial neural
networks
N. Pasadakis, V Gaganis, Ch. Foteinopoulos
Abstract
Gasoline, the key profit generator for the petroleum refining industry, is produced by
blending different fuel streams coming from various production processes. The blend
recipes are determined such that the properties’ specifications of the final gasoline are
met, while maximizing the profitability of the product under the constraint of the
gasoline components availability. Therefore, optimum control on gasoline blending
operations is a key question in the refineries.
In this work Artificial Neural Network (ANN) models have been developed to predict
the Research Octane Number (RON) of gasoline blends produced in a Greek refinery.
The developed ANN models use as input variables the volumetric content of seven
most commonly used fractions in the gasoline production and their respective RON
numbers. The predicting ability of the models in the multi-dimensional space
determined by the input variables was thoroughly examined in order to asses its
robustness. Based on the developed ANN models, the effect of each gasoline
constituent on the formation of the blend RON value was revealed. Additionally, an
optimization algorithm has been developed to estimate the composition of a blend
displaying any desired RON value, optimized with respect to the production cost and
availability of the involved gasoline fractions.