Detection of fuel adulteration using wave optical with machine learning algorithms

Abstract

Fuel is a very important factor and has considerable influence on the air quality in the environment, which is the heart of the world. The increase of vehicles in lived-in areas results in greater emission of carbon particles in the environment. Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution. Adulteration is the mixing of foreign substances in fuel, which damages vehicles and causes more health problems in living beings such as humans, birds, aquatic life, and even water resources by emitting high levels of hydrocarbons, nitrogen oxides, and carbon monoxide. Most frequent blending liquids are lubricants and kerosene in the petrol, and its adulteration is a considerable problem that adds to environmental pollution. This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms. A modified evanescent wave optical fiber sensor with discrete wavelet transform is proposed for classification of adulterated data from the samples. Furthermore, support vector machine classifier is used for accurate categorization. The sensor is first tested with fuel and numerical data is classified based on machine learning algorithms. Finally, the result is evaluated with less error and high accuracy of 99.9%, which is higher than all existing techniques.