Dynamic prediction of sulfur dioxide concentration in a single-tower double-circulation desulfurization system based on chemical mechanism and deep learning

Abstract

With the fluctuation of load in coal-fired power plants, the sulfur dioxide (SO₂) concentration in the flue gas changes more and more frequently, due to the large delay and inertia characteristics of wet flue gas desulfurization (WFGD) systems, the SO₂ concentration in the flue gas emitted from the outlet is unstable. To accurately control the SO₂ emission concentration of the desulfurization system, a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system outlet SO₂ concentration prediction model was established. Firstly, considering the application of the established model in control systems and system optimization operation schemes, it is necessary to enhance the interpretability of the model. Therefore, based on the chemical reaction process of SO₂ absorption in the desulfurization system, SO₂ mechanism prediction models for the outlet of the absorption tower and absorber feed tank (AFT) tower were established, and parameter identification was carried out using quantum particle swarm optimization algorithm (QPSO) and historical operation data of the power station. Secondly, to improve the prediction accuracy of the mechanism model, a Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM)-Attention data compensation model was established. In this process, the difficulty of manually adjusting the hyperparameters of the deep learning model was considered, during the model training process, the rime optimization algorithm was used to optimize the model hyperparameters in real time. To enable the data compensation model to obtain more data feature information, the input data was decomposed using the Variational Mode Decomposition (VMD) method and the same frequency modes were combined and reconstructed. After model training, different modal model outputs were superimposed to obtain the final compensation data. Finally, the mechanism model and data compensation model were combined to obtain a hybrid prediction model for SO₂ concentration. The model validation results showed that the error indicators root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) of the model are 0.2739 mg/m³, 0.8267%, and 0.9999, respectively.