STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Mechanical Dynamics and Systems Analysis Original Research

Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend

  1. Kiran Dinkar Chaudhari Department of Data Science, Shri Vile Parle Kelavani Mandal’s Narsee Monjee Institute of Management Studies (SVKM’s NMIMS), Mukesh Patel School of Technology Management and Engineering, Mumbai
  2. Kailas Dhanraj Deore Department of Data Science, Shri Vile Parle Kelavani Mandal’s Narsee Monjee Institute of Management Studies (SVKM’s NMIMS), Mukesh Patel School of Technology Management and Engineering, Mumbai
  3. Kshamta Mathur Department of Data Science, Shri Vile Parle Kelavani Mandal’s Narsee Monjee Institute of Management Studies (SVKM’s NMIMS), Mukesh Patel School of Technology Management and Engineering, Mumbai

Abstract

Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration to gain valuable insights into optimizing engine performance and emissions for alternative fuel blends, advancing knowledge in this field. Four machine learning algorithms—decision tree, random forest, linear regression, and XGBoost—were employed to predict engine performance (brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT)) and emission parameters (CO, CO2, HC, NOx, smoke) using data from experiments with various biodiesel blends (B10, B20, B30) derived from waste cooking oil and diesel, incorporating different calcium oxide nanoparticle concentrations. The models were trained and tested using a 70/30 split of a dataset comprising engine load, additive concentration, and biodiesel blend ratio as input variables. Model performance was evaluated using R², mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). With regression coefficient (R2), MSE, MAE, MAPE, and RMSE values of 0.845, 255.7, 5.66, 0.1645, and 7.49 percent, respectively, in overall prediction, the XGBoost model performed better in engine performance prediction. This study demonstrates the effectiveness of XGBoost in optimizing biodiesel blends and nanoparticle additives for enhanced engine performance and reduced emissions, offering a more efficient alternative to traditional experimental methods. Future research could explore more advanced machine learning techniques and validate the model with a larger dataset.

Keywords

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