基于RBF神经网络的流向变换催化燃烧反应器的温度预测

TEMPERATURE PREDICTION BASED ON RBF NEURAL NETWORKS FOR REVERSE FLOW REACTOR WITH CATALYTIC COMBUSTION OF CONTAMINANTS

用RBF神经网络建立了用于清除低浓度挥发性有机物的流向变换催化燃烧反应器拟定态温度分布模型 .从基于过程机理模型的数值计算结果出发 ,结合中试装置的实时操作数据建立了拟定态床层温度的人工神经元网络深层知识库 ,用于增强神经网络模型的“外推能力”和“可信度” .仿真结果表明所建立的模型简单、精度高 ,能满足特性预测的要求 .

The quasi-steady state model of temperature profile for the reverse flow reactor with catalytic combustion of air contaminated with volatile organic compounds (VOCs), is developed by RBF (radial basis function) neural networks. The deep knowledge repository of temperature profile is yielded based on a large number of numerical solutions of the determinant mathematical model, which increases the “extrapolative ability” and “reliability”. Simulation results have proved that the model presented in this paper i...