Performance and emission predictions of a CRDI engine powered with diesel fuel: A combined study of injection parameters variation and Box-Behnken response surface methodology based optimization. (15th April 2021)

• Record Type: Journal Article
• Authors: Ramachander, J.; Gugulothu, S.K.; Sastry, G.R.K.; Kumar Panda, Jibitesh; Surya, M. Siva
• Is Part Of: Fuel. Volume 290(2021)
• Publication Date: 2021-04-15
• Subjects: Diesel -- Injection timings -- Injection pressure -- ANOVA -- Response Surface Methodology -- Performance and Emission; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2020.120069
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: Performance, combustion and emissions of a CRDI diesel engine were investigated. HC and NOx have been decreased for lower FIP. At SOI of 342.5 CA, lower exhaust gas temperature is observed. The response surface methodology helps to find optimum factors. 'Regression Model' is best fit with standard deviation of 0.095, adjusted R2 of 0.972 and adequate precision of 18.482. Abstract: The operating mode of reactivity-controlled compression ignition appears as an effective technique for improving the engine's efficiency and emission reduction. This study investigates the emission and combustion characteristics of diesel engines operating under the reactivity-controlled compression ignition mode. The primary objective of this research is to examine the effect of fuel injection timings (7.5, 12.5 and 17.5 bTDC) and injection pressure (500 and 1000 bar), experimental testing is carried out on single cylinder water-cooled testing engines at constant speed of 1500 rpm with variable engine load (16, 20 and 24 Nm). In case of a fuel injection pressure of 1000 bar, the maximum brake specific fuel consumption of 0.42 kg/kWh is registered with a brake mean effective pressure of 3.2 bar. In this experimental study, Box-Behnken based response surface methodology was used to predict the optimal input parameters, resulting in the optimal combination of output and emission parameters. In addition, a statistically relevant test analysis of variance has been developed to obtain a 'regression model.' Results have shown that the proposed 'Regression Model' is ideally suited to 0.095 standard deviation, 0.972 modified R2 and 18.482 acceptable accuracy. This analysis also attempts to describe the application of the response surface methodology analysis to optimize the emission and performance parameters.