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 vehi-cles in lived-in areas results in greater emission of carbon par...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 vehi-cles in lived-in areas results in greater emission of carbon particles in the envir-onment.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 environ-mental pollution.This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms.A modified evanescent wave opticalfiber sensor with discrete wavelet transform is proposed for classification of adult-erated data from the samples.Furthermore,support vector machine classifier is used for accurate categorization.The sensor isfirst tested with fuel and numerical data is classified based on machine learning algorithms.Finally,the result is eval-uated with less error and high accuracy of 99.9%,which is higher than all existing techniques.展开更多
文摘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 vehi-cles in lived-in areas results in greater emission of carbon particles in the envir-onment.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 environ-mental pollution.This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms.A modified evanescent wave opticalfiber sensor with discrete wavelet transform is proposed for classification of adult-erated data from the samples.Furthermore,support vector machine classifier is used for accurate categorization.The sensor isfirst tested with fuel and numerical data is classified based on machine learning algorithms.Finally,the result is eval-uated with less error and high accuracy of 99.9%,which is higher than all existing techniques.
