Experimental Investigation on the Effect of Graphene Oxide in Higher Alcohol Blends and Optimization of Injection Timing Using an ANN Method

Abstract

The use of alternative fuels in diesel engines has become more widespread due to a number of factors, including dwindling petroleum supplies, increasing prices for conventional fossil fuels, and environmental worries about pollutants and greenhouse gas emissions from internal combustion engines. Efficiency and emissions need to be appropriately balanced. Alcohols act as oxygenated fuels similar to octanol, offering a number of benefits over traditional fuels and can boost efficiency, enhance combustion, and reduce air pollution. Therefore, the research aimed to enhance the performance and combustion characteristics of a diesel and octanol blend using graphene oxide (GO) nanoparticles as a fuel additive in a single-cylinder diesel engine while reducing emissions. Research findings will contribute significantly to improving the physical and chemical properties of diesel and octanol blends, thereby mitigating the challenges of limited petroleum reserves and environmental concerns. A range of different blends of diesel and octanol were prepared on a volume/volume basis in proportions of D70OCT30, D60OCT40, and D50OCT50, and then GO was added as a fuel additive to the abovementioned blends in varied proportions (40, 60, and 80 ppm) resulting in nine blends. These blends were analyzed in terms of various performance, combustion, and emission characteristics, and the obtained results helped to shed light on the impact of GO as a fuel additive. The results indicated that the fuel blend D70OCT30GO0.006 yielded the highest values. Furthermore, it is highly imperative that we develop a model that can be used to predict engine behavior and its stability without having to run an engine. For this, a data-driven artificial neural network (ANN) model was developed to predict the optimized injection timing for better combustion and reduced emission. The efficiency and prediction capabilities of the model were compared to the experimental data, which indicated that the ANN model had a better prediction score. The injection timing of the engine was optimized from 21 °CA to 21.5 °CA, which increased the efficiency by 1%. The research findings showed significantly improved physical and chemical properties of the blends, thereby mitigating the challenges of limited petroleum reserves and environmental concerns.