化工学报2016，Vol.67Issue(8)：3481-3490,10.DOI:10.11949/j.issn.0438-1157.20160370
基于人工神经网络和遗传算法的甲烷制氢催化剂设计
Catalyst design for production of hydrogen from methane based on artificial neural network and genetic algorithm
摘要
Abstract
By screening the auxiliary components and preparation methods, a Fe3O4 composite oxide catalyst was prepared for production of hydrogen from methane. Based on the properties of neural network, an improved back-propagation network model was developed to simulate the relationship between components of the catalyst and catalytic performances of the catalyst life and the formation rate of hydrogen. The model network structure and the computer-aided design procedures were established after investigation of the structural organization, the training method, the activation function, and the generalization ability of artificial neural network. Upon used the Levenberg-Marquardt training method, the network convergence was improved significantly and a formulation model of artificial neural network was achieved with strong generalization capability. To further enhance efficiency of catalyst design, a hybrid genetic algorithm was employed for global optimization of design. After six cycles of design optimization, a series formulation of Fe3O4 composite oxide catalysts for production of hydrogen from methane was developed. When one of the optimized catalysts was applied in hydrogen production, the catalyst life and formation rate of hydrogen were 4.46 h and 1.16 mmol·min−1·(g Fe)−1, respectively, which were better than those of previously reported catalysts.关键词
甲烷/制氢/催化剂/人工神经网络/遗传算法Key words
methane/hydrogen production/catalyst/artificial neural network/genetic algorithm分类
化学化工引用本文复制引用
黄凯,陈勇,母志为,何跃..基于人工神经网络和遗传算法的甲烷制氢催化剂设计[J].化工学报,2016,67(8):3481-3490,10.基金项目
国家重点基础研究发展计划项目（2012CB21500402）；国家自然科学基金项目（21576049）；中央高校基本科研业务费专项资金项目（2242016k40082）；东南大学基于教师科研的本科生科研训练计划项目（T16192022）。@@@@supported by the National Basic Research Program of China (2012CB21500402), the National Natural Science Foundation of China (21576049), the Fundamental Research Funds for the Central Universities (2242016k40082) and the Student Research Training Program of Southeast University (T16192022) （2012CB21500402）
