Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopy

M. Krishnamoorthi and R. Malayalamurthi

Energy, 2018, vol. 165, issue PB, 1292-1319

Abstract: This work describes the performance, combustion and emission behavior of chaulmoogra oil blend and neat diesel in the variable compression ratio, variable speed compression ignition engine with exhaust gas recirculation (EGR). Exergy analysis was employed to investigate availability shares involved in the research engine. Artificial neural network (ANN) modeling and particle swarm optimization (PSO) are adopted with response surface methodology (RSM) in order to investigate the engine performance fuelled with ternary blend (65% diesel+25% chaulmoogra oil+10% diethyl ether) and neat diesel. In this work, compression ratio (CR) is varied from 14.5 to 20.6, engine speed varied from 1500 to 2400 revolution per minute (rpm) and EGR varied from 0 to 30%. The optimized condition was observed as 10% EGR, 18.1CR and 1672 rpm with respect to lesser exhaust emissions and enhanced thermal efficiency. Maximum brake thermal efficiency of 29.12% was observed 10% EGR rate and maximum exergy efficiency of 52.64% was observed for the ternary blend at the optimized engine condition. The results conclude that the RSM-ANN-PSO provide better the engine performance modeling with acceptable accuracy. The corrosion and wear analyses were done on the fuel injector nozzle and cylinder gaskets using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS).

Keywords: ANN; Chaulmoogra oil; Compression ratio; EGR; RSM-PSO; SEM/EDS (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218321029
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:energy:v:165:y:2018:i:pb:p:1292-1319

DOI: 10.1016/j.energy.2018.10.112

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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