Publication Type : Journal Article
Publisher : International Journal of Innovative Technology and Exploring Engineering (IJITEE)
Source : International Journal of Innovative Technology and Exploring Engineering (IJITEE) , Volume 9, Issue 7 (2020)
Url : https://www.ijitee.org/wp-content/uploads/papers/v9i7/G5707059720.pdf
Keywords : Data Pre-processing, Engine Emissions, Linear regression, Machine Learning.
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2020
Abstract : A large set of data are being generated in engine testing which is used for the evaluation of performance and prediction of emission characteristics. For any engine modifications or required improvements in the results, the whole testing procedure to be repeated again for further evaluation. To overcome this repetition, we need some data handling and analysis techniques such as machine learning and prediction models. The datasets which were collected by testing procedures help in building a prediction model by which the expected results of the test can be predicted without conducting repeated trials. This study mainly focusses on predicting the emissions of a diesel engine using a prediction model built by Linear Regression Algorithm in Machine Learning using Regression Learner Application in MATLAB. Linear Regression prediction model was built from the emission data collected from the single-cylinder diesel engine testing. The prediction model is validated and compared with the actual testing data obtained. Errors such as RMSE, MSE, MAE, R-squared errors are evaluated and found to be minimum. Using a validated prediction model, the emissions values can be predicted for any range of data set. This will reduce the time and cost involved by the repetition of testing procedures.
Cite this Research Publication : K. Venkatesh and Sivanesan Murugesan, “Prediction of Engine Emissions using Linear Regression Algorithm in Machine Learning”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , vol. 9, no. 7, 2020.