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Prediction of Cetane Number of a Biodiesel Based on Physical Properties and a Study of Their Influence on Cetane Number

机译:基于物理性质的生物柴油的十六烷数量及其对十六烷值的影响预测

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Cetane number is one of the indispensable parameters in the study and selection of fuels for CI engines. Hence it is an important criterion for selection of bio-diesels, which exhibit a wide variety of characteristics based upon their source, method of preparation, etc. Since the conventional techniques for evaluating cetane number are tedious, alternate methods are being developed. This paper attempts to find cetane number based on the properties of the bio-diesel so that cetane number can be found without operating an engine. If a correlation between fuel properties and cetane number is established, the influence of each of the fuel properties on cetane number can be analyzed. This paper uses artificial neural networks (ANNs), which are a recently developed computational technique used to correlate non-linear data, to predict cetane number and analyze the influence of the various fuel properties namely density, viscosity, flash and fire points on the cetane number of a bio-diesel and its various blends. Oils produced from seeds of Sunflower, Palm, Pungam plant (Honge) and their Methyl Esters are used for this analysis. Standard diesels with known cetane numbers are used. The fuel properties are found by standard techniques. Different combinations of ANNs are formed by varying the number of hidden layer neurons, activation functions and other network parameters and are trained to predict cetane number from fuel properties using a series of train data. The networks so formed are validated using a series of test data. The best network is chosen based on the least value of mean squared error. By analyzing the weight matrix of the trained network, the relative impacts of the properties on the cetane number are analyzed. It is hoped that this work would aid in the selection of the suitable source and method of preparation to obtain the bio-diesel with the desired physical properties so that optimal cetane number is achieved.
机译:十六烷值是CI发动机研究和选择的必不可少的参数之一。因此,它是选择生物柴油的重要标准,其基于它们的来源,制备方法等具有各种特性。由于用于评估十六烷数量的常规技术是乏味的,正在开发替代方法。本文试图根据生物柴油的特性找到第五烷数量,以便在不操作发动机的情况下找到十六烷值。如果建立燃料特性和十六烷数之间的相关性,则可以分析每种燃料特性对十六烷值的影响。本文采用人工神经网络(ANNS),其是最近开发的用于关联非线性数据的计算技术,以预测十六烷值,分析各种燃料特性的影响即密度,粘度,闪光点对十六烷生物柴油的数量及其各种共混物。从向日葵,棕榈,旁普氏植物(珩磨)和其甲酯种子产生的油用于该分析。使用具有已知十六烷值的标准柴油。通过标准技术发现燃料特性。通过改变隐藏层神经元,激活功能和其他网络参数的数量来形成不同的ANN的不同组合,并且训练以使用一系列列车数据从燃料特性预测十六烷值。如此形成的网络使用一系列测试数据进行验证。基于均方误差的最小值选择最佳网络。通过分析训练网络的重量矩阵,分析了十六烷值上的性质的相对影响。希望这项工作有助于选择合适的来源和制备方法,以获得具有所需物理性质的生物柴油,从而实现最佳的十六烷值。

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