Dynamic Prediction for SO₂ Concentration in SD-WFGD System Based on Mechanism-Data Combined Model

In order to achieve the economic operation and ensure that the SO₂ emission concentration of desulfurization system meets environmental requirements in a coal-fired unit, a combined dynamic prediction model was established for SO₂ concentration at the outlet of the single-tower double-cycle wet fuel gas desulfurization (SD-WFGD) system. Firstly, the chemical reaction process of absorbing SO₂ was analyzed, and the SO₂ concentration mechanism models at the outlet of the absorption tower and absorber feed tank (AFT tower) were established respectively. Secondly, the historical data and mechanism deviation data were decomposed using variational mode decomposition (VMD) method, and the decomposed arrays with different frequencies were reconstructed. After which, models with different modal components were trained and obtained based on least square support vector machine (LSSVM) algorithm, while a data compensation model was established based on adaptive weight allocation strategy by the weighted stacking of LSSVM models with different modal components. Finally, a combined dynamic prediction model for SO₂ concentration in the SD-WFGD system was obtained through the superposition of the outputs of dynamic compensation model and mechanism model. Results show that after the decomposition of historical data into different modes by VMD algorithm, the prediction accuracy of data model can be improved effectively with the reconstruction of data in different modes. Meanwhile, the model prediction ability can be improved with the combination of mechanism model and data model.