Bolanča Tomislav, Marinović Slavica, Ukić Sime, Jukić Ante, Rukavina Vinko
Acta Chim Slov. 2012 Jun;59(2):249-57.
This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.
本文描述了人工神经网络模型的开发，该模型可用于将几种傅里叶变换红外光谱-衰减全反射（FTIR-ATR）吸光度和拉曼强度作为输入变量，来关联和预测柴油燃料的特性。多层前馈神经网络和径向基函数神经网络已被用于快速同时预测商业柴油燃料的十六烷值、十六烷指数、密度、粘度、10%（T10）、50%（T50）和90%（T90）回收温度下的蒸馏温度、总芳烃和多环芳烃含量。在本研究中，应用了多层前馈网络的两阶段训练程序。虽然第一阶段训练算法始终是反向传播算法，但对第二阶段的两种训练算法进行了变化和比较，即共轭梯度算法和拟牛顿算法。对于径向基函数网络，使用K均值径向分配算法和三种不同的径向扩展算法（显式、各向同性和K近邻算法）对径向层进行训练。为了通过减少不必要的实验工作来确保良好的预测能力，对两种神经网络的隐藏层神经元数量和用于训练集的实验数据点进行了优化。这项工作表明，所开发的人工神经网络模型可以基于单次快速红外或拉曼测量同时确定柴油燃料的主要特性。
