A data-driven soft sensor model for coal-fired boiler SO2 concentration prediction with non-stationary characteristic

Environmental issues have received significant global attention, and achieving ultra-low emission has become a crucial goal for thermal power plant. Pollutant prediction is essential to achieving ultra-low emission control. Due to the frequent adjustment of operating conditions, the coal-fired power generation process is a typical nonstationary process with complex changes along the time direction. A single model cannot effectively portray the complex non-stationary characteristics and model mismatch may occur in the prediction, which brings new challenges for modeling coal-fired SO2 emission. Therefore, the condition-driven category boosting (CatBoost) soft sensor model is proposed for real-time prediction of SO2 concentration. First, the thermal process time series is rearranged into different data slices. These data slices vary along the direction of the condition indicator, which can characterize the process characteristics on different slices. Second, the load-based sequential condition division (LSCD) algorithm is designed to realize the division of boiler operation process. The characteristics of process are effectively captured by variational auto-encoder (VAE), and the data slices with similar process characteristics are merged into the same mode. Non-stationary process along the time direction is reorganized into different condition modes along the load direction. Finally, the high-resolution local CatBoost models under different modes are constructed to achieve fine-grained prediction of SO2 concentration. The effectiveness of the proposed condition-driven model is verified by an industrial example of 1000 MW coal-fired boiler. The prediction results show that the proposed model can effectively divide the operation conditions and achieve accurate prediction of SO2 concentration. In the typical models in SO2 predicting, the proposed model has better performance with a determination coefficient of 0.92.