Elkelawy Medhat, El Shenawy E A, Bastawissi Hagar Alm-Eldin, Shams Mahmoud M, P V Elumalai, Balasubramanian Dhinesh, Anand Vivek, Alwetaishi Mamdooh
Mechanical Power Engineering Departments, Faculty of Engineering, Tanta University, Tanta, Egypt.
Mechanical engineering department, Faculty of Engineering, Pharos University in Alexandria (PUA), Alexandria, Egypt.
Sci Rep. 2024 Dec 16;14(1):30474. doi: 10.1038/s41598-024-77234-8.
Due to the restrictions of the diesel engine emissions and the massive demand of energy, the fossil diesel fuel has been consumed quickly and the resources cannot suffice the demand. Alternative fuels that include bio alcohols, hydrogen and biodiesel can make up the diesel fuel depletion. Biodiesel is convenient for diesel engine operation due to its properties like fossil diesel properties. Response surface methodology is a statistical approach for responses prediction and optimization using definite number of experiments to provide time and cost. This study aims to predict and optimize the performance and emission attributes; of diesel engine has single cylinder and operates at 1400 rpm constant speed fuelled with pure diesel fuel or diesel fuel blended with waste cooked oil (WCO) biodiesel at different blending ratio by using response surface methodology (RSM). The influences of the independent variables that are WCO biodiesel blend percentages and the diesel engine load values on the responses that are predicted and optimized. The WCO biodiesel/ diesel fuel blend percentages are pure diesel fuel without biodiesel (B0), 40% WCO biodiesel with 60% diesel fuel (B40) and 80% WCO biodiesel with 20% diesel fuel (B80). The experiments are performed using diesel engine runs at 1400 rpm constant speed, at varying diesel engine loads are zero, 4 and 8 kW. The design of experiments (DOE) is attempted using central composite design (CCD). The RSM model is a nonlinear model developed according to the independent variables and the responses. The homogeneity between the independent variables is studied to predict and optimize their influences on the diesel engine performance and emission attributes. The RSM model is validated according to the coefficients of regression are R, R adjusted and the R predicted that prove the satisfaction of the results. From the experiments it is observed that diesel engine performance and emissions attributes are enhanced by increasing the diesel engine load value and increasing the percentage of WCO biodiesel blending ratio compared to pure diesel only like NO emissions which reduced from 1200 ppm to 900 ppm at the same engine load due to the reduced combustion temperatures using WCO biodiesel. According to the response optimizer tool, the optimal responses are 17.11% for the BTE, 658.9 ppm for the NO emissions and 1.93% for CO emission at independent variables are 2.6667 kW diesel engine load and 100% pure diesel fuel.
由于柴油发动机排放的限制以及能源的大量需求，化石柴油燃料消耗迅速，资源无法满足需求。包括生物醇、氢气和生物柴油在内的替代燃料可以弥补柴油燃料的枯竭。生物柴油因其具有类似化石柴油的特性，便于柴油发动机运行。响应面法是一种统计方法，通过一定数量的实验来预测和优化响应，以节省时间和成本。本研究旨在通过响应面法（RSM）预测和优化单缸柴油发动机的性能和排放特性，该发动机以1400转/分钟的恒定转速运行，使用纯柴油或与废食用油（WCO）生物柴油按不同混合比例混合的柴油作为燃料。研究了自变量（WCO生物柴油混合比例和柴油发动机负载值）对预测和优化响应的影响。WCO生物柴油/柴油燃料混合比例分别为无生物柴油的纯柴油（B0）、40%WCO生物柴油与60%柴油燃料（B40）以及80%WCO生物柴油与20%柴油燃料（B80）。实验在柴油发动机以1400转/分钟的恒定转速运行时进行，柴油发动机负载分别为零、4千瓦和8千瓦。尝试使用中心复合设计（CCD）进行实验设计（DOE）。RSM模型是根据自变量和响应建立的非线性模型。研究自变量之间的同质性，以预测和优化它们对柴油发动机性能和排放特性的影响。根据回归系数R、调整后的R和预测的R对RSM模型进行验证，证明结果令人满意。从实验中观察到，与仅使用纯柴油相比，通过增加柴油发动机负载值和提高WCO生物柴油混合比例，可以提高柴油发动机的性能和排放特性，例如在相同发动机负载下由于使用WCO生物柴油降低了燃烧温度使得NO排放量从1200 ppm降至900 ppm。根据响应优化工具，在自变量为2.6667千瓦柴油发动机负载和100%纯柴油燃料时，最佳响应为：制动热效率（BTE）为17.11%，NO排放量为658.9 ppm，CO排放量为1.93%。
