Publication Type : Journal Article
Publisher : Renewable Energy (Scopus, Thomson Reuters Indexed (Impact factor: 6.274)),
Source : Renewable Energy (Scopus, Thomson Reuters Indexed (Impact factor: 6.274)), Volume 134, p.579-602 (2019)
Url : https://www.sciencedirect.com/science/article/pii/S0960148118313818
Keywords : ANN, Chaulmoogra oil, compression ratio, EGR, Optimization, Speed
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2019
Abstract : This study investigates the performance, combustion and emission characteristics of diesel (D)/straight vegetable oil (SVO)/diethyl ether (DEE) blend in a variable compression ratio engine (VCR), variable engine speed direct injection compression ignition (CI) engine. The compression ratio (CR), speed (N), and load (L) were taken as input factors for the diesel engine optimization. This investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. The response surface methodology (RSM) optimization concerned to minimize the engine emissions and maximize the engine performance. Three optimization works were conducted for 65% D+25% SVO+10% DEE blend, in optimization-1 (opti-1) considered 0% exhaust gas recirculation (EGR), Opti-2 considered 5% EGR and Opti-3 considered 10% EGR. Compared to diesel, carbon monoxide (CO), oxides of nitrogen (NOx), and hydrocarbon (HC) were reduced by 12.8%, 4.19% and 9.61% respectively for blend fuel in opti-2.
Cite this Research Publication : M. Krishnamoorthi, Malayalamurthi, R., and Sakthivel R., “Optimization of compression ignition engine fueled with diesel - chaulmoogra oil - diethyl ether blend with engine parameters and exhaust gas recirculation”, Renewable Energy (Scopus, Thomson Reuters Indexed (Impact factor: 6.274)), vol. 134, pp. 579-602, 2019.