Predicting model of CH4-CO2 reforming on Ni/Al2O3 catalyst by improved back propagation（BP）neural network

doi:10.16085/j.issn.1000-6613.2016-2126

Abstract

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.

Key words: CH₄-CO₂ catalytic reforming, BP neural networks, simulation, optimization, prediction

摘要： CH₄-CO₂重整制合成气的反应可以实现CO₂的减排和C₁资源的高效利用。这一反应受反应温度、原料气比例、催化剂种类等诸多因素影响，如果考察每种因素影响，势必大大增加实验的工作量。人工神经网络以其超强容错性、并行处理、可学习和自适应等优点，在非线性预测方面具有明显的优势。本文基于Ni/Al₂O₃催化CH₄-CO₂重整反应过程，采用改进的BP神经网络建立了关于CH₄转化率、CO₂转化率和H₂/CO比的预测模型，结果表明，采用改进BP神经网络在此研究中具有更快的收敛速度和更好的网络稳定性，其收敛次数仅为BP神经网络的58.86%；改进BP神经网络模型的敏感度分析表明，输入因素对反应结果影响的顺序为：反应温度 > Ni负载量 > 平均孔径≈比表面积≈孔体积。

关键词: CH₄-CO₂催化重整, BP神经网络, 模拟, 优化, 预测

CLC Number:

TE646

FU Ke, XIE Liangcai, YAN Yuyuan, LI Bo, HE Gai, XU Long, MA Xiaoxun. Predicting model of CH₄-CO₂ reforming on Ni/Al₂O₃ catalyst by improved back propagation（BP）neural network[J]. Chemical Industry and Engineering Progress, 2017, 36(07): 2393-2399.

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(07): 2393-2399.

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Predicting model of CH₄-CO₂ reforming on Ni/Al₂O₃ catalyst by improved back propagation（BP）neural network

改进BP神经网络预测Ni/Al₂O₃催化CH₄-CO₂重整反应

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