Soft sensing of SO2 emission for ultra-low emission coal-fired power plant with dynamic model and segmentation model

Abstract

Static models, segmentation models and dynamic models for soft sensing of SO2 emission were developed based on the Ordinary Least Squares, Support Vector Regression, eXtreme Gradient Boosting, Random Forest and Neural Network algorithms with real operational data from a 1000 MW coal-fired power plant with ultra-low emission systems. The prediction results of test set show that static models are not suitable for modeling the desulfurization systems with complex condition and time-delay process. Thus, segmentation models and dynamic models were used to optimize the prediction accuracy. All the prediction performance of the nonlinear models was improved significantly with dynamic model, while the prediction performance of the linear model was improved with segmentation model. The dynamic Neural Network model with one hidden layer achieves the most accurate regression result (R2 = 70.4 %, RMSE = 1.95 mg/m3, MAE = 1.53 mg/m3). The results show the superiority of the dynamic Neural Network model over the other and the dynamic Support Vector Regression model perform second-best.