Implementation of Predictive Modelling Techniques for determining Exhaust Engine Emissions. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Khurana, Shivansh; Saxena, Shubham; Jain, Sanyam; Dixit, Ankur
• Is Part Of: Journal of physics. Volume 1854:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1854/1/012028
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The world is going through the biggest change in modern world, the climate change. And this is majorly due to our utmost reliance over fossil fuels. Researchers, industrialists and scientists around the globe are trying to figure out the alternate energy sources or at least the ones with the least poisonous emissions. To design an automobile machinery with such parameters that they would cause possibly the least pollution, a huge sum of money is spent on majorly executing experimentations repeatedly. The paper has shown the objective of testing of such emissions and with the help of known machine learning algorithms, predicting those harmful emissions. In other words, using statistical modelling to predict the engine emissions which is traditionally measured by an exhaust gas analyser. In this research, data of real-time engine emissions produced by burning of Bio-Diesel fuel is recorded and fed into machine learning algorithms for their training. Three machine learning emission models were built up to illustrate their emission ranges. Out of the three, results showed that Decision Tree based engine emission model showed the best results.