Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling

作     者：Feng Hua Zhou Fang Tong Qiu 

作者机构：Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and ApplicationBeijing 100084China 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2018年第26卷第12期

页      面：2562-2572页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 

基　　金：Supported by the National Natural Science Foundation of China(U1462206) 

主　　题：Convolutional neural network Network motif Naphtha pyrolysis Kinetic modeling 

摘      要：System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network *** the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.