A Novel Hybrid CNN-LSTM Scheme for Nitrogen Oxide Emission Prediction in FCC Unit

Fluid Catalytic Cracking (FCC), a key unit for secondary processing of heavy oil, is one of the main pollutant emissions of NO x in refineries which can be harmful for the human health. Owing to its complex behaviour in reaction, product separation, and regeneration, it is difficult to accurately predict NO x emission during FCC process. In this paper, a novel deep learning architecture formed by integrating Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for nitrogen oxide emission prediction is proposed and validated. CNN is used to extract features among multidimensional data. LSTM is employed to identify the relationships between different time steps. The data from the Distributed Control System (DCS) in one refinery was used to evaluate the performance of the proposed architecture. The results indicate the effectiveness of CNN-LSTM in handling multidimensional time series datasets with the RMSE of 23.7098, and the R 2 of 0.8237. Compared with previous methods (CNN and LSTM), CNN-LSTM overcomes the limitation of high-quality feature dependence and handles large amounts of high-dimensional data with better efficiency and accuracy. The proposed CNN-LSTM scheme would be a beneficial contribution to the accurate and stable prediction of irregular trends for NO x emission from refining industry, providing more reliable information for NO x risk assessment and management.