化工进展2017，Vol.36Issue(7)：2393-2399,7.DOI:10.16085/j.issn.1000-6613.2016-2126
改进BP神经网络预测Ni/Al2O3催化CH4-CO2重整反应
Predicting model of CH4-CO2 reforming on Ni/Al2O3 catalyst by improved back propagation (BP) neural network
摘要
Abstract
CH4-CO2 reforming reaction can produce synthesis gas,which is an ideal way both for the reduction of CO2 emission and the efficient utilization of C1 resources.This reaction is affected by many factors,such as reaction temperature,ratio of raw material gas,catalyst type and so on.If each of factors were investigated,it would greatly increase the workload of the experiment.Artificial neural network(ANN) has obvious advantages in nonlinear prediction because of its superior fault tolerance,parallel processing and adaptive learning.The prediction model about CH4-CO2 reforming reaction catalyzed by Ni/Al2O3 was built based on artificial neural network.This model was trained by back propagation (BP) algorithm and improved BP algorithm,respectively.It was found that the improved BP model was much better than the BP model in view of the stability and convergence speed.Compared with the BP algorithm,the improved BP algorithm reduced the number of convergence times greatly,which was only 58.86％ of that in BP model.By sensitivity analysis of the models,it showed that the reaction temperature was the most important factor on the reaction indexes (CH4 conversion,CO2 conversion,and H2/CO ratio) among five input factors,followed by Ni loading.In addition,the average pore size,the specific surface area,and the pore volume had relatively small effects on reaction indexes within the experimental range.关键词
CH4-CO2催化重整/BP神经网络/模拟/优化/预测Key words
CH4-CO2 catalytic reforming/BP neural networks/simulation/optimization/prediction分类
能源科技引用本文复制引用
付柯,谢良才,闫雨瑗,李波,贺改,徐龙,马晓迅..改进BP神经网络预测Ni/Al2O3催化CH4-CO2重整反应[J].化工进展,2017,36(7):2393-2399,7.基金项目
国家自然科学基金重点项目(21536009)及西安市科技计划项目(CXY1511(4)). （21536009）
