Response surface methodology-based parameter optimization of single-cylinder diesel engine fueled with graphene oxide dosed sesame oil/ diesel fuel blend

作     者：Suleyman Simsek Samet Uslu Hatice Simsek 

作者机构：Istanbul Aydin UniversityMechanical Engineering Department˙IstanbulTurkey Karabuk UniversityMechanical Engineering DepartmentKarabukTurkey Tekirdag Namik Kemal UniversityMachinery and Metal Technologies DepartmentTekirdagTurkey 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2022年第10卷第4期

页      面：126-135页

核心收录：

学科分类：080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：No financial support was received from any institution or organization for this study 

主　　题：Graphene oxide Nanoparticle Sesame oil Biodiesel Diesel RSM 

摘      要：In this study, an experimental study was carried out to determine the effects of adding different amounts of graphene oxide (GO) on engine characteristics to a single-cylinder diesel engine operating with 30% sesame oil (SO) + 70% diesel fuel mixture. After that, an optimization was carried out with response surface methodology (RSM) to determine optimum operating conditions at different engine loads. Experimental results showed that GO nanoparticle is a good addition for diesel–biodiesel blends to enhance the performance and reduce emissions. The most appropriate amount of GO is between 75 ppm and 100 ppm for the performance characteristics. The optimal amount of GO for power is 75 ppm, while for brake-specific fuel consumption (BSFC) and exhaust gas temperature (EGT) it is 100 ppm. In addition, the maximum GO amount of 100 ppm is the most suitable for carbon monoxide (CO) and hydrocarbon (HC), and 75 ppm GO amount is the most appropriate for nitrogen oxides (NOx). On the other hand, optimization results revealed that 100 ppm GO at 1950 W load was optimum conditions for all responses. The responses that emerged under optimum conditions were 1746.77 W, 968.73 g/ kWh, 259.8 ⁰C, 0.0603%, 23.13 ppm and 185.61 ppm for power, BSFC, EGT, CO, HC, and NOx, respectively. According to the validation study, the error between the optimum and experimental results is 4.69% maximum. According to the findings of study, it can be concluded that the RSM model can successfully model a singlecylinder diesel engine and thus save time, and money.