Petroleum Science >2010,??Issue 1:??118-122 DOI: https://doi.org/10.1007/s12182-010-0015-y
Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil Open?Access
文章信息
作者：Dong Xiucheng,Wang Shouchun,Sun Renjin and Zhao Suoqi
作者單位：
School of Business Administration, China University of Petroleum, Beijing 102249, China;School of Business Administration, China University of Petroleum, Beijing 102249, China;School of Business Administration, China University of Petroleum, Beijing 102249, China;School of Chemical Science and Engineering, China University of Petroleum, Beijing 102249, China
投稿時(shí)間：2009-03-16
引用方式：Dong, X., Wang, S., Sun, R. et al. Pet. Sci. (2010) 7: 118. https://doi.org/10.1007/s12182-010-0015-y
文章摘要
Accurate prediction of chemical composition of vacuum gas oil (VGO) is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks (ANN) based on a genetic algorithm (GA) is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.
關(guān)鍵詞
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Saturates, vacuum gas oil, prediction, artificial neural networks, genetic algorithm