Estimating fuel adulteration in automobiles using robust optical fiber sensors

Abstract

The significance of petroleum product consumption in most countries is growing owing to different reasons like urbanization, population increase, development events and life style changes, which in turn leads to prevalent environment pollution. Adulterant refers to the substance that gets added to another and may not be legally allowed in most of the cases. A petroleum fuel is one such case which is vulnerable to adulteration specifically for improving the profit margins. Detection of this petroleum fuels adulteration is challenging as they are naturally present in the compounds already. The compositional variations of these fuels are determined using various physico-chemical properties measurements. For discriminating the adulterated samples from the unaltered ones, the statistical designs along with the data mining help. Monitoring of the quality of fuel is essential at the distribution point for the prevention of adulteration. We propose to use a fuel adulterations setup that is portable, in expensive and is capable of providing the results in a short time. This includes the use of a light weight optical fiber sensor that gives high performance with low attenuation and there are no fire hazards, as well as they are resistant to harsh environments for testing. The distilled curves along with principal component analysis and support vector machine based classification helps us to build a model that is capable of this adulteration detection.