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

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.

Experimental Study on Sustainability Involving the Pugh Matrix on Emission Values of High-Temperature Air in the Premixed Charged Compression Ignition Engine.

The main aim of the study was to reduce carbon emissions in the atmosphere using a novel Andropogon narudus (AN) biofuel using higher air temperatures and reducing the consumption of conventional fossil fuel (diesel). The use of a heat exchange chamber within the air intake manifold is a popular method to reduce hydrocarbon (HC) and carbon monoxide (CO) emissions during cold starts. A premixed charged compression ignition engine in the dual-fuel mode was used in this study with raw diesel, raw AN oil, AN70+D30, AN80+D20, AN80+D20 (35 °C), AN80+D20 (40 °C), and AN80+D20 (45 °C). A chamber was designed and analyzed to measure the exit temperature and density change and to determine the reduction in volumetric efficiency of the engine, using Ansys Fluent software. A sustainability assessment study was performed to understand the feasibility of the fuel and the design using the Pugh Matrix. The fuel AN80+D20 with an air temperature of 45 °C was found to be superior to all other fuels in terms of brake thermal efficiency, reaching at 32.1%. D100 used the least amount of energy, whereas AN80+D20 used the most. Engine HC emission was at the lowest (45.01 ppm) for AN80+D20 fuel at 45 °C air input and reached the highest (50 ppm) for AN100 fuel. With an air temperature of 45 °C, CO emission was at its lowest for AN80+D20 gasoline (0.018%) and was at its highest for AN100 (0.072%). Nitrogen oxide emissions were the highest for AN80+D20 fuel with an air temperature of 45 °C, with an air concentration of 1254 ppm, whereas they were the lowest for AN100 (900 ppm). CO2 values were reduced, with D100 showing the lowest levels and AN100 showing the highest. The smoke emission was minimum for AN80+D20 fuel at 45 °C, with a smoke number of 15 compared to 33 for D100 fuel. As per the Pugh Matrix assessment, AN80+D20 with 35 °C air temperature had higher scores compared to all of the other fuel mixtures.

Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels.

This study is focused on artificial neural network (ANN) modelling of non-modified diesel engine keyed up by the combination of two low viscous biofuels to forecast the parameters of emission and performance. The diesel engine is energised with five different test fuels of the combination of citronella and Cymbopogon flexuous biofuel (C50CF50) with diesel at precise blends of B20, B30, B40, B50 and B100 in which these numbers represent the contents of combination of biofuel and the investigation is carried out from zero to full load condition. The experimental result was found that the B20 blend had improved BTE at all load states compared with the remaining biofuel blends. At 100% load state, BTE (31.5%) and fuel consumption (13.01 g/kW-h) for the B20 blend was closer to diesel. However, the B50 blend had minimal HC (0.04 to 0.157 g/kW-h), CO (0.89 to 2.025 g/kW-h) and smoke (7.8 to 60.09%) emission than other test fuels at low and high load states. The CO2 emission was the penalty for complete combustion. The NOx emission was higher for all the biodiesel blends than diesel by 6.12%, 8%, 11.53%, 14.81% and 3.15% for B20, B30, B40, B50 and B100 respectively at 100% load condition. The reference parameters are identified as blend concentration percentage and brake power values. The trained ANN models exhibit a magnificent value of 97% coefficient of determination and the high R values ranging between 0.9076 and 0.9965 and the low MAPE values ranging between 0.98 and 4.26%. The analytical results also provide supportive evidence for the B20 blend which in turn concludes B20 as an effective alternative fuel for diesel.

Production of eco-friendly fuel with the help of steam distillation from new plant source and the investigation of its influence of fuel injection strategy in diesel engine.

The primary intention of this experiment is to abate the harmful emissions of imported petroleum fuel by approach of novel citronella emulsified fuel. The study is emphasized by evaluating the influence of alteration in IT (injection timing) and IP (injection pressure) in diesel engine when utilizing B20 emulsion fuel of 5% water, 1% surfactant, 14% citronella oil and 80% diesel. The IT and IP are speckled in the array of 21 degCA bTDC, 23 degCA bTDC and 25 degCA bTDC and 180, 200, 220 and 240 bar correspondingly. It is found that retarding the IT and increasing the IP along with emulsified fuel lead to increase in the brake thermal efficiency by 1.16% and minimal in the brake-specific fuel consumption by 4.86% at top load state when correlated with diesel. Exhaust emissions carbon monoxide, NOx and smoke were considerably reduced by 35%, 0.8% and 34% respectively, but slight increase in HC was observed by 5.26%; heat release rate and cylinder pressure had a considerable improvement. From the determination of these values, the optimum values of IT and IP are inferred as 21° bTDC and 200 bar. Graphical abstract.