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Detection of Gasoline Adulteration Using Modified Distillation Curves and Artificial Neural Network
Author(s) -
Foroughi Babak,
Shahrouzi Javad Rahbar,
Nemati Ramin
Publication year - 2021
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.202000217
Subject(s) - gasoline , distillation , diesel fuel , artificial neural network , metering mode , principal component analysis , correlation coefficient , volume (thermodynamics) , mathematics , process engineering , biological system , chromatography , chemistry , engineering , artificial intelligence , computer science , statistics , automotive engineering , waste management , thermodynamics , mechanical engineering , physics , biology
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.