Machine Learning Optimization-Based Efficient Detection of Fuel Adulteration Using a Novel Circular Slotted Refractive Index Sensor

The overall quality of fuel significantly impacts the durability and optimal performance associated with all fuel engines. Several irresponsible retailers adulterate lower-priced oily substances or components with fuel compounds to augment their earnings. The Symmetric Quarter-Arc Optical Refractive Index Sensor (SQAORIS) has been developed to determine adulteration in fuel to address this issue. This fuel adulteration includes petrol, kerosene, and Diesel. The approach of neural network regression in machine learning has also been studied to showcase the actual value and predicted value of fuel adulteration. Its own unique quarter-arc design gives accurate and faster results while detecting the smallest variations caused by adulteration. It achieved optimum sensitivity values of 1623.52 nm/RIU, 1616.82 nm/RIU, 1614.17 nm/RIU, and detection limit values of 0.000419, 0.000202, 0.000386 for adulterated petrol, kerosene, and diesel, respectively. The optimum values for quality factors are 1433.55, 848.71, 1479.24, 866.90, and the optimum values for Fig. of merits are 1068.10, 594.69, 1016.86, 586.97 for water, petrol, kerosene, and diesel adulteration, respectively. The optimum detection range of 1865.33 was achieved for kerosene adulteration. We have observed an optimum value of 0.9984 from machine learning prediction by the neural network regression method. In addition, due to its enhanced sensitivity and facilitating features, the sensor is going to serve a crucial role in practical uses in the near future.