A neural network approach for the correlation of exhaust emissions from a diesel engine with diesel fuel properties

Karonis, D; Lois, E; Zannikos, F; Alexandridis, A; Sarimveis, H

dc.contributor.author	Karonis, D	en
dc.contributor.author	Lois, E	en
dc.contributor.author	Zannikos, F	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:18:32Z
dc.date.available	2014-03-01T01:18:32Z
dc.date.issued	2003	en
dc.identifier.issn	0887-0624	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15074
dc.subject	Diesel Engine	en
dc.subject	Exhaust Emissions	en
dc.subject	Diesel Fuel	en
dc.subject	Neural Network	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Cetane	en
dc.subject.other	Distillation curve	en
dc.subject.other	Exhaust emissions	en
dc.subject.other	Kinematic viscosity	en
dc.subject.other	Single cylinder diesel engine	en
dc.subject.other	Carbon monoxide	en
dc.subject.other	Density (specific gravity)	en
dc.subject.other	Diesel fuels	en
dc.subject.other	Distillation	en
dc.subject.other	Gas emissions	en
dc.subject.other	Hydrocarbons	en
dc.subject.other	Neural networks	en
dc.subject.other	Nitrogen oxides	en
dc.subject.other	Particles (particulate matter)	en
dc.subject.other	Sulfur	en
dc.subject.other	Diesel engines	en
dc.title	A neural network approach for the correlation of exhaust emissions from a diesel engine with diesel fuel properties	en
heal.type	journalArticle	en
heal.identifier.primary	10.1021/ef020296p	en
heal.identifier.secondary	http://dx.doi.org/10.1021/ef020296p	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	This paper presents expressions correlating the exhaust emissions from a single-cylinder diesel engine with some of the most important properties of the fuels used, using a neural network approach. The exhaust emissions measured were carbon monoxide, hydrocarbons, nitrogen oxides, and particulate matter. The experiments were performed using a matrix of 59 fuels. The cetane number of the fuels covered the range 42-58, the density varied between 0.840 and 0.860 g/mL, and the sulfur content from 0.05 to 0.20 wt %. The predictions were based on specific points of the distillation curve, the cetane number, density, and kinematic viscosity of the fuels. In the case of particulate matter emissions, sulfur content was also employed. The predictions obtained were very good for all the emissions considered. The aromatic content was not used as a predictor variable, because it was found to have a strong inter-correlation with the cetane number, density, and two specific points of the distillation curve, the 50% and the 90% recovery point.	en
heal.publisher	AMER CHEMICAL SOC	en
heal.journalName	Energy and Fuels	en
dc.identifier.doi	10.1021/ef020296p	en
dc.identifier.isi	ISI:000185462200018	en
dc.identifier.volume	17	en
dc.identifier.issue	5	en
dc.identifier.spage	1259	en
dc.identifier.epage	1265	en