摘要：为了提高工业生产中煤粉质量流量检测的测量精度,在双弯管法测量固相质量流量原理的基础上,利用人工神经网络优良的非线性映射能力,将固相质量流量难以确定的影响因素反映到网络的连接权值中,建立基于径向基函数网络的软测量模型;在气力输送煤粉实验平台上进行实验,获取实验数据,以实验数据为样本对模型进行训练与仿真。结果表明,模型测量误差不超过3%,是煤粉固相质量流量测量的一种简单、有效的方法。

关键词：煤粉；质量流量；双弯管法；径向基函数网络

Abstract: To improve the measurement accuracy of coal powder mass flow measurement in industrial production， based on the double-elbow method of measuring mass flow， a radial basis function network soft measurement model was established using the powerful nonlinear mapping ability of artificial neural network and reflecting difficult determined influence factors of solid mass flow to network connection weights. The experiment was conducted on the coal powder pneumatic conveying experiment platform to get experimental data. The model was trained and simulated taking experimental data as samples. The results show that the measure error of the model is within 3% which provides a simple and effective method for solid phase mass flow measurement of coal powders. 

Keywords: coal powders； mass flow； double-elbow method； radial basis function network