Dynamic prediction of SO2 emission based on hybrid modeling method for coal-fired circulating fluidized bed. (15th August 2023)

• Record Type: Journal Article
• Authors: Chen, Jiyu; Gao, Mingming; Zhang, Hongfu; Yu, Haoyang; Yue, Guangxi
• Is Part Of: Fuel. Volume 346(2023)
• Publication Date: 2023-08-15
• Subjects: Coal-fired CFB -- SO2 prediction -- GRU -- Hybrid modeling method; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2023.128284
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The connection between the mechanism model and the single-layer gated neural network model was analyzed. This paper proposed a new deep learning model for predicting SO2 concentration in CFB units. The validity and superiority of the model are verified through ablation experiments and comparative experiments. Abstract: Pollutant prediction for coal-fired circulating fluidized bed units is crucial for ultra-low emission optimization. Accurate prediction models can assist in the control optimization of the unit. Mechanism models are limited by the determination of parameters, coefficients, and fitting functions in the model and require a large amount of operational and unit design data in practical applications. With the development of deep learning, more and more deep learning models are used in parameter prediction. These models suffer from insufficient prediction accuracy when performing parameter prediction tasks due to the lack of a priori knowledge of the mechanism process. This paper analyzed the relationship between the differential equation model under the first-order Taylor expansion and the single-layer Gated Recurrent Unit neural network model. According to the analysis results, this paper proposed a mixed prediction model of SO2 concentration. The ablation study demonstrated the validity of the predictive model structure. The operation datasets of two actual units were used for verification. In terms of MAE indicators, the results of the proposed model on the two data sets are 124.5669 mg/Nm 3 and 178.0473 mg/Nm 3 . In terms of MAPE indicators, the results of the proposed model on the two data sets are 5.85% and 14.07%.