基于RBFNN的焦化烟气脱硫脱硝过程建模

Modeling of desulfurization and denitrification process for coking flue gas based on RBFNN

基于某焦化公司烟气脱硫脱硝一体化装置的运行数据,研究此过程的静态建模方法。依据工艺原理及相应数据预处理方法构建数据集,通过K-均值聚类算法实现对过程工况的划分与样本集的约简,利用神经网络对脱硫与脱硝过程的每一工况分别进行静态建模,仿真结果表明此方法及相应神经网络模型有效。

Based on the running data of the integrated desulfurization and denitrification unit, static modeling is studied for the process. The data set is established according to the process and data preprocessing method. Classification of the working conditions and simplification of the sample set are realized by K-means clustering algorithm. Static modeling of each and every working condition is established with the help of neural network. The simulation result shows that this method and corresponding neural network are effective.