Prediction of selected biodiesel fuel properties using artificial neural network  

作　　者：Solomon O. GIWA Sunday O. ADEKOMAYA Kayode O. ADAMA Moruf O. MUKAILA 

机构地区：[1]Department of Agricultural and Mechanical Engineering, College of Engineering and Environmental Studies, Olabisi Onabanjo University, lbogun Campus, Ifo, Ogun State, Nigeria

出　　处：《Frontiers in Energy》2015年第4期433-445,共13页能源前沿（英文版）

摘　　要：Biodiesel is an alternative fuel to replace fossil- based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network （ANN） has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid （FA） composition. In this study, four ANNs have been designed and trained to predict the cetane number （CN）, flash point （FP）, kinematic viscosity （KV） and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenie acids except for density network with a sixth parameter （temperature）. Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN （96.69%; 1.637%）, KV （95.80%; 1.638%）, FP （99.07%; 0.997%） and density （99.40%; 0.101%）. These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.Biodiesel is an alternative fuel to replace fossil- based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network （ANN） has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid （FA） composition. In this study, four ANNs have been designed and trained to predict the cetane number （CN）, flash point （FP）, kinematic viscosity （KV） and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenie acids except for density network with a sixth parameter （temperature）. Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN （96.69%; 1.637%）, KV （95.80%; 1.638%）, FP （99.07%; 0.997%） and density （99.40%; 0.101%）. These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.

关 键 词：BIODIESEL fuel properties artificial neuralnetwork fatty acid PREDICTION 

分 类 号：TQ336.1[化学工程—橡胶工业] TP183[自动化与计算机技术—控制理论与控制工程]