RSM-based optimization of diesel-hazelnut biodiesel-diethyl ether ternary fuel blends for engine performance, combustion, and emission characteristics

The use of alternative fuels in diesel engines is an important area of research for ensuring energy sustainability. Ternary fuel blends formed by combining biodiesel fuels with oxygen-containing additives offer the potential to improve the combustion process and reduce emissions. In this study, the effects of ternary fuel blends consisting of neat diesel, hazelnut oil biodiesel (HB), and diethyl ether (DEE) additives on performance, combustion characteristics, and exhaust emissions in a single-cylinder diesel research engine were investigated using experimental and statistical approaches. In the experimental design process, response surface methodology (RSM) was employed, and the experimental points were determined using the central composite design (CCD) method. The independent variables considered were engine load (11-22 Nm), HB ratio (10-20%), and DEE ratio (0-5%). The response parameters evaluated included indicated mean effective pressure (IMEP), indicated thermal efficiency (ITE), brake specific fuel consumption (BSFC), maximum pressure rise rate (MPRR), combustion duration (CD), as well as carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) emissions. Second-order regression models representing the response parameters were developed using experimental data; the statistical adequacy of the models was examined using analysis of variance (ANOVA), and the interactions between the parameters were analyzed using contour plots. According to the results of the RSM-based optimization study, the optimal operating conditions for the diesel research engine were determined to be an engine load of 18.8 Nm, an HB ratio of 19 vol%, and a DEE additive ratio of 5 vol%. The multi-response optimization yielded a combined desirability of 0.867. Validation experiments performed under the optimum operating conditions showed good agreement with the predicted values, with an average prediction error of 3.97%, supporting the predictive capability of the developed RSM models within the investigated design space. The results indicate that the Diesel-HB-DEE ternary fuel blend can improve combustion efficiency, reducing fuel consumption and the investigated exhaust emissions under the tested operating conditions.