Detection of Gasoline Adulteration Using Modified Distillation Curves and Artificial Neural Network

To detect adulteration in gasoline, an automatic distillation apparatus was set up to measure the recovered volume and temperature simultaneously. The level metering was performed by online image processing instead of the conventional visual operator-based measurement. To investigate the effect of additives in super gasoline, regular gasoline and diesel were added and the distillation curves were analyzed. The principal component analysis model was employed to reduce the obtained data. Finally, an artificial neural network was applied to predict the volume percentage of adulterants in super gasoline. Statistical analysis showed that the proposed model has a mean relative error and correlation coefficient of 4.6 % and 0.995, respectively.

Automated summary

An automatic distillation apparatus and online image processing were utilized to detect gasoline adulteration. The study revealed significant impacts of additives on super gasoline. By employing principal component analysis and artificial neural network, accurate prediction of adulterants' volume percentage was achieved.