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

Foroughi, B.; Shahrouzi, Javad Rahbar; Nemati, Ramin

doi:10.1002/ceat.202000217

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Authors in [ 12 ] presented a study on detecting gasoline adulteration utilizing altered distillation curves and artificial neural networks (ANN). Their study was conducted to find the temperature and recovered volume simultaneously.…”

Section: Methodsmentioning

confidence: 99%

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Sujatha

Chatterjee

Priyadarshini

et al. 2022

Int J Comput Intell Syst

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Any nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.

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Section: Methodsmentioning

confidence: 99%