Virtual Reactor and Dynamic Reaction Analysis by Using Neural Networks

利用神经网络进行虚拟反应器和动态反应分析

• 批准号: 11650793
• 负责人: TSUTSUMI Atsushi
• 金额: $ 2.3万
• 依托单位: University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11650793/
• 关键词: Neural Networks; Virtual Reactor; Virtual Reaction Experiment; Dynamic Reaction Engineering; Nonlinear Systems; Virtual Reaction Systems; 仮想反応; 仮想反応実験; ニュートラルネットワーク

In the present study, artificial neural networks have been applied to modeling the reaction kinetics of carbon-NOx reaction as a "virtual reaction system". The unsteady-state kinetic experiments were carried out using a fixed-bed flow reactor at various different experimental conditions altered continuously. The changes in the concentrations of O2, CO2, N2, NO and NO2 were measured as time series data of reaction kinetics by gas chromatography and a NOx meter. The time series data of reaction kinetics (the amount of catalyst and carbon, temperature and concentrations of reaction species versus time) was used for learning the neural networks. The mechanisms of carbon-NOx reaction are discussed based on the virtual experiments by means of trained neural network model.

在本研究中，人工神经网络已被应用到模拟的反应动力学的碳-氮氧化物反应作为一个“虚拟反应系统”。采用固定床流动反应器，在各种不同的实验条件下进行了非稳态动力学实验。O2、CO2、N2、NO和NO2浓度的变化作为反应动力学的时间序列数据通过气相色谱和NOx计测量。反应动力学的时间序列数据（催化剂和碳的量，温度和反应物种的浓度随时间的变化）用于学习神经网络。利用训练好的神经网络模型，在虚拟实验的基础上，对碳氮反应机理进行了探讨。