Integration of Encoding and Temporal Forecasting: Toward End-to-End NOx Prediction for Industrial Chemical Process

Forecasting NOx concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Although process features and cross-series correlations can be captured through feature extraction techniques, they are commonly linear transformation, and conducted or trained separately from forecasting model. This process is inefficient and might not be an optimal solution for the following forecasting modeling. Therefore, we propose a time series encoding temporal convolutional network (TSE-TCN). By parameterizing the hidden representation of the encoding-decoding structure with the temporal convolutional network (TCN), and combining the reconstruction error and the prediction error in the objective function, the encoding-decoding procedure and the temporal predicting procedure can be trained by a single optimizer. The effectiveness of the proposed method is verified through an industrial reaction and regeneration process of an FCC unit. Results demonstrate that TSE-TCN outperforms some state-of-art methods with lower root mean square error (RMSE) by 2.74% and higher R-2 score by 3.77%.

Automated summary

Forecasting NOx concentration in FCC regeneration flue gas can guide real-time adjustment of treatment devices and prevent excessive pollutant emissions. The proposed method, TSE-TCN, combines encoding-decoding and temporal predicting procedures using a single optimizer, resulting in improved accuracy compared to state-of-art methods.