Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel

D. Babu, Vinoth Thangarasu and Anand Ramanathan

Applied Energy, 2020, vol. 263, issue C, No S0306261920301240

Abstract: The present work investigates the influence of advanced injection strategy on a common rail direct injection assisted diesel engine characteristics fuelled with biodiesel and conventional diesel. Also, an artificial neural network is employed to forecast engine characteristics. Engine test is conducted under 100% load condition through an optimized nozzle opening pressure of 500 bar. Pre-injection timing is fixed permanently at 30 °CA bTDC, main injection timing varied from 15 °CA to 21 °CA bTDC and post-injection varied from 6 °CA bTDC to 6 °CA aTDC sequentially. However, the pre, main and post-injection quantities are changed respectively from 5% to 15%, 70% to 90%, and 5% to 15%. Minimum carbon monoxide, unburned hydrocarbon and smoke emission of 0.01% vol., 8 ppm and 1.59 FSN are achieved with pre-injection timing of 30 °CA, main injection timing of 21 °CA bTDC, and post injection timing of 6 °CA bTDC for B100-15%-70%-15%. Maximum brake thermal efficiency and nitric oxide emission of 34.3% and 1114 ppm are achieved in B100-5%-90%-5% at advanced injection timing and higher nozzle opening pressure. Artificial neural network models conform to experimental results having a lower root mean square error and correlation coefficient values in a range of 0.01 to 0.02 and 0.980 to 0.998 respectively. An artificial neural network is mostly preferred over other theoretical and empirical models to higher accuracy in predicting the output. Hence, multiple injection strategy fuelled biodiesel significantly decreased the emission and improved the performance compared to mechanical, single and split injection strategy.

Keywords: Artificial neural network; Combustion; Common rail direct injection system; Methyl ester; Multiple injection (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920301240
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301240

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.114612

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().