Modelling of performance, emission, and combustion of an HCCI engine fueled with fusel oil-diethylether fuel blends as a renewable fuel. (15th April 2021)

• Record Type: Journal Article
• Authors: Safieddin Ardebili, Seyed Mohammad; Solmaz, Hamit; Calam, Alper; İpci, Duygu
• Is Part Of: Fuel. Volume 290(2021)
• Publication Date: 2021-04-15
• Subjects: Fusel oil -- Diethyl ether -- HCCI -- Combustion -- Renewable fuel -- Response surface method; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2020.120017
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The impact of fusel oil-diethylether on the engine performance/emissions studied. Response surface methodology was used to model an HCCI engine. The optimal operating condition were determined. Abstract: The aim of this study is to model the HCCI engine performance and exhaust emissions characteristics fueled with fusel oil/diethylether fuel as a renewable fuel by employing the response surface method. The effect of independent variables —different concentrations of fusel oil/diethylether fuel, engine speed, and lambda value — on the response parameters including engine torque, BSFC, COV imep, MPRR, along with CO2, CO, NOX, and UHC were investigated and estimated by multi-regression models. To determine an optimal combination of engine working condition, the desirability function approach was used. High desirability of 82% was achieved at the diethyl ether ratio of 41.72%, the engine speed of 884 rpm, and the lambda value of 2.08. This engine working condition was recommended as the optimum response variables for the HCCI engine having 11.80 Nm of torque, 1.36% of COVimep, 3.14 of MPRR, BSFC of 268 g/kWh, CA10 of 7.52, and CA50 of 11. Besides, the optimal value for engine-out emissions was found to be 0 ppm for NOX, 243.11 ppm for UHC, 6.09 (%Vol.) for CO2, and 0.2 (%Vol.) for CO emissions. The outcomes of this study indicated that all multi-regression models developed by the RSM method could successfully estimate the variations of both engine performance indicators and exhaust emissions.