Research and Application of NOx Emission Prediction Model of FCC Unit Based on LSTM Network [基于LSTM的催化裂化装置NOx排放预测模型及应用]

作者：He W.; Tang Z.; Wu B.; Luan H.; Zhang J.; Chen C.; Liang H.

来源：Journal of Xi'an Shiyou University(Natural Science Edition), 2020, 35(4): 108-113.

DOI：10.3969/j.issn.1673-064X.2020.04.017

• FCC
• LSTM network
• NOx emission
• Prediction model

Because of the complexity of FCC process, to adjust its process parameters is very easy to cause chain reaction.It is difficult to predict the pollution emission of FCC processby using the traditional lumped model.In order to solve the problem of mutual coupling of multi parameters in the mass production data and pollution emission data of refining and chemical enterprises, the main influence factors of NOx emission are determined using principal component analysis (PCA), and they are nitrogen content in raw material, reaction temperature, ratio of catalyzer to crude oil and residence time of raw material.NOx emission prediction model is established using long-short term memory(LSTM) network, and the NOx emission of a 3.5 million ton RFCC unit is predicted using it.The prediction result of LSTM network is compared with those of CNN, SVM and BP neural networks.It is shown that, considering the internal data characteristics of time series, the mean absolute error, root mean square error, Pearson correlation coefficient and determinable coefficient of the prediction result of LSTM network are better than those of CNN, SVM and BP neural networks.

• 单位
  中国石油大学（华东）