Prediction of Performance and Emission Attributes of Biodiesel Blends in a Single Cylinder Engine Using Artificial Neural Networks
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 5 | Views: 211 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Analysis Study Research Paper | Computer and Mathematical Sciences | India | Volume 13 Issue 3, March 2024 | Popularity: 5.2 / 10


     

Prediction of Performance and Emission Attributes of Biodiesel Blends in a Single Cylinder Engine Using Artificial Neural Networks

Dr Kiran Kumar Billa


Abstract: The present investigation delivers a comprehensive viewpoint on the current artificial intelligence (AI) meta-modelling in diesel engine system, particularly in the domains of multi-objective optimization. A Lavenberg-Marquardt feed-forward backpropagation learning algorithm was perceived to be good for ANN. The ANN model revealed its adeptness with a higher degree of accuracy between the predicted and experimental datasets and demonstrated a good agreement. The strength of the model is assessed using conventional metrics as well as some sophisticated metrics like MAPE, MSRE and NSE. The AI model showed satisfactory results with MAPE of 0.577-2.01% and acceptable RMSE threshold of 0.0093-0.0324. The special error metrics MSRE was 0.0000951-0.00013, NSE was 0.9967-0.9996, and Theil U2 0.019-0.055.


Keywords: Artificial intelligence, diesel engine optimization, Accuracy metrics, error analysis


Edition: Volume 13 Issue 3, March 2024


Pages: 824 - 828


DOI: https://www.doi.org/10.21275/SR24311142310


Please Disable the Pop-Up Blocker of Web Browser

Verification Code will appear in 2 Seconds ... Wait



Text copied to Clipboard!
Dr Kiran Kumar Billa, "Prediction of Performance and Emission Attributes of Biodiesel Blends in a Single Cylinder Engine Using Artificial Neural Networks", International Journal of Science and Research (IJSR), Volume 13 Issue 3, March 2024, pp. 824-828, https://www.ijsr.net/getabstract.php?paperid=SR24311142310, DOI: https://www.doi.org/10.21275/SR24311142310

Top