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Analysis on Emissions and Performance of Ceramic Coated Diesel Engine Fueled with Novel Blends Using Artificial Intelligence

机译:利用人工智能,用新型混合燃料陶瓷涂层柴油机排放与性能分析

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The exhaustive nature of petroleum products triggers the obstacles of scarcity, economic imbalance, and environmental depletion. It is difficult to avoid their usage all of a sudden and switch to clean electric prime movers. Under all these circumstances, the researchers may initiate their investigations on alternative fuels for preeminent solution. The present study covers the performance and emissions of a single cylinder, four-stroke, diesel engine fueled with Pongamia pinnata and Calophyllum inophyllum biodiesels added with n -butanol additive at various proportions. In this investigation, the piston has been coated with ceramic material with a thickness of 200? μ m topcoat. The blends have been tested at 1500?rpm speed and rated compression ratio of 17.5?:?1 at various operating loads. A comparative result analysis has been made on the engine parameters operated by diesel and showed that mechanical efficiency gradually increases with a percentile increment of n -butanol in the blend. Moreover, emissions such as CO, CO 2 , NO x , and opacity were found to be reduced for the samples having high amount of n -butanol, whereas HC emissions slightly increased. In addition, all the exhaust gases have been predicted by using second-order polynomial equations generated and artificial Intelligence technique, and the comparative analysis has been made. It has been identified that ANN showed an average accuracy of prediction superior than regression analysis.
机译:石油产品的详尽性质触发了稀缺,经济不平衡和环境枯竭的障碍。很难避免使用所有突然的用途并切换到清洁的电力推动器。在所有这些情况下,研究人员可以在卓越的解决方案中启动他们对替代燃料的调查。本研究涵盖了单缸,四冲程,柴油发动机的性能和排放,燃料柴油机与Pongamia Pinnata和Calophyllum inophyllum生物柴油以各种比例加入N-丁醇添加剂。在该研究中,活塞已涂有厚度为200的陶瓷材料? μm面漆。共混物以1500°速度和额定压缩比为17.5?:1在各种操作负荷下。对柴油操作的发动机参数进行了比较结果分析,并显示了在混合物中逐渐增加的机械效率随百分比的N-丁醇逐渐增加。此外,发现诸如CO,CO 2,NO X和不透明度的排放量被降低,用于具有大量N-丁醇的样品,而HC排放略微增加。另外,通过使用产生的二阶多项式方程和人工智能技术来预测所有废气,并且已经进行了比较分析。已经确定了ANN显示出比回归分析优于预测的平均精度。

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