AI Summary & Key Takeaways
AI InsightThe study to verify the re-sults obtained from the optimization study reveals that the results were ob-tained with an error of less than 9%.
📌 3 Key Takeaways
Abstract
In this study, an optimization study was carried out by using Response Surface Methodology (RSM) to determine the optimum conditions by im-proving the working conditions in a single cylinder diesel engine using fuel blends created by mixing the biodiesel obtained from grape seed oil (GSO) to diesel in different proportions (5%, 10% and 15% by vol.). Experiments were carried out with three different fuel mixtures with three different injection pressures (200, 225 and 250 bar) at three different engine loads (400, 1000 and 1600-Watt). Since the minimum number of experiments proposed by the RSM application is 20 for optimization according to three different fac-tors and three different levels of each factor, an RSM model was created from the experiment data obtained by performing 20 trials. While the GSO ratio, the injection pressure and engine load was determined as input factors, brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), carbon monoxide (CO), hydrocarbon (HC), nitrogen oxides (NOx) and smoke were chosen as responses on the RSM model. Considering the findings taken from the RSM model, the working conditions in which the best output can be obtained from the engine; it has been determined as 13% GSO percentage, 245 bar injection pressure and 850-W engine load. The study to verify the re-sults obtained from the optimization study reveals that the results were ob-tained with an error of less than 9%.