UPM Institutional Repository

Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks


Citation

Rajak, Upendra and Panchal, Manoj and Dasore, Abhishek and Verma, Tikendra Nath and Chaurasiya, Prem Kumar (2024) Predict the characteristics of the DI engine with various injection timings by Glycine max oil biofuel using artificial neural networks. Environmental Science and Pollution Research. ISSN 0944-1344; eISSN: 1614-7499

Abstract

Glycine max oil biofuel (GMOB) is a product of the transesterification of soybean oil. It contains a substantial amount of thermal energy. In this study, the result of varying fuel injection timings on the performance, ignition, and exhaust parameters of a research engine with single-cylinder, four-stroke with direct injection (DI) diesel was experimentally investigated and optimised using artificial neural networks (ANN). The results demonstrated that a 20% fuel blend with 24.5° before top dead centre (b TDC) decreased brake thermal efficiency (BTE), NOx emissions, and exhaust cylinder temperature but improved fuel consumption, carbon dioxide emissions (CDE), and smoke emissions. With 26.5° b TDC, the BTE was found to be approximately 5.0% higher while the fuel consumption was approximately 2.0% lower than with the original injection timing of 24.5° b TDC. At 26.5° b TDC, the NOx emission was approximately 8.6% higher, and the smoke emission was approximately 4.07% lower than at the original injection timing (24.5° b TDC).


Download File

Full text not available from this repository.

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1007/s11356-024-34429-w
Publisher: Springer
Keywords: Artificial neural network; Diesel engine; Glycine max oil biofuel; Pollutant formation
Depositing User: Mohamad Jefri Mohamed Fauzi
Date Deposited: 05 Feb 2025 07:28
Last Modified: 05 Feb 2025 07:28
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/s11356-024-34429-w
URI: http://psasir.upm.edu.my/id/eprint/113930
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item