Abstract:The growing demand for fossil fuels, the rise in their price and many environmental concerns strengthen the incessant search for fuel alternatives. Recently, traffic noise has been described as a threat to human health and the environment, being responsible for premature deaths. In this context, the usage of alcohol/diesel fuel blends in diesel engines has gained increasing impact as a substitute fuel for use in internal combustion engines. Moreover, alcohol can be derived from environmentally friendly process… Show more
“…Another important problem of diesel engines is mechanical vibrations and noise emissions [22][23][24]. These problems negatively affect driving comfort and harm people and the environment [25,26].…”
The use of alcohol-derived fuels produced from renewable resources is an effective method to reduce dependence on petroleum. However, alcohols can improve the combustion process by changing the fuel chemistry. In this way, performance, emission, mechanical vibration and noise values can be improved in diesel engines. In this study; New fuel forms (D90E10, D90IB10, D80E10IB10, D77.5E10IB10DEE2.5, 75E10IB10DEE5) were formed by mixing ethanol, isobutanol and diethyl ether alcohols with diesel fuel in certain proportions. The fuels generated was used in experiments. The studies were conducted with four different loads (%25, 50, 75, and 100) at a constant speed (2800 rpm). The optimum fuel mixture was determined by examining the engine performance, exhaust emissions, mechanical vibrations and noise data obtained in the experiments. When the most important data output of the test results is evaluated; In tests with D75E10IB10DEE5 fuel, it was determined that smoke emissions were reduced by 24.6% and mechanical vibrations by 14.2% compared to standard diesel fuel at full load.
“…Another important problem of diesel engines is mechanical vibrations and noise emissions [22][23][24]. These problems negatively affect driving comfort and harm people and the environment [25,26].…”
The use of alcohol-derived fuels produced from renewable resources is an effective method to reduce dependence on petroleum. However, alcohols can improve the combustion process by changing the fuel chemistry. In this way, performance, emission, mechanical vibration and noise values can be improved in diesel engines. In this study; New fuel forms (D90E10, D90IB10, D80E10IB10, D77.5E10IB10DEE2.5, 75E10IB10DEE5) were formed by mixing ethanol, isobutanol and diethyl ether alcohols with diesel fuel in certain proportions. The fuels generated was used in experiments. The studies were conducted with four different loads (%25, 50, 75, and 100) at a constant speed (2800 rpm). The optimum fuel mixture was determined by examining the engine performance, exhaust emissions, mechanical vibrations and noise data obtained in the experiments. When the most important data output of the test results is evaluated; In tests with D75E10IB10DEE5 fuel, it was determined that smoke emissions were reduced by 24.6% and mechanical vibrations by 14.2% compared to standard diesel fuel at full load.
“…However, the main source of noise is known as diesel engine, especially in vehicles with diesel engines. Studies to reduce diesel engine noise have increased in importance in recent years [4]. On the other hand, the fact that these values are high is also important for the health of the internal combustion engine operating for a long time [5,6].…”
Reducing noise and vibration emissions, which are the most important factors affecting drivingcomfort in diesel engine vehicles, is an important issue. It seems possible to reduce these emissions byadding renewable fuels to diesel fuel. The main purpose of this study is to reduce engine vibration and noiseemissions by mixing ethanol and isopropanol fuels with diesel fuel. In the experimental study, 7% ethanol(D93E7) and 7% isopropanol (D93IP7) were added to the pure diesel fuel to reduce vibration and noiseemissions caused by the use of standard diesel (D100) fuel. The experiments were carried out at twodifferent loads (3-6 Nm) and at four different speeds (1000-1500-2000-2500 rpm). When the test resultsare evaluated in general; In the tests performed with D93E7 fuel at 6 Nm load and 2000 rpm, it wasdetermined that vibration emissions decreased by 26% compared to D100 fuel and noise emissionsdecreased by 2%. On the other hand, in tests performed at low load (3 Nm) with D93I
“…Another method is to blend the fuels with selective oxygenate additives, such as alcohols and ethers, that can reduce soot formation during combustion. A number of studies have shown that oxygenates, such as methanol [10][11][12], ethanol [10,13,14], n-butanol [15,16], n-octanol [17], dimethyl ether [18], polyoxymethylene dimethyl ether (PODE) [19][20][21][22], and diethyl ether [23][24][25], can reduce soot formation in IC engines. The propensity of oxygenated compounds to reduce soot is not only dependent on the oxygen content, but also strongly depends on the molecular structure, as shown by a number of works [26][27][28][29][30][31].…”
The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.
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