Hybrid Modeling and Real-time Predictive Scheduling of Wet Flue Gas Desulfurization for Energy Saving and Life Extension

Sulfur dioxide (SO2) is one of the most important air pollutants emitted by power plants and ironworks. To effectively remove SO2 from flue gas, wet flue gas desulfurization (WFGD) technology is widely adopted by most of China's coal-fired power plants. However, one of the most important issues of WFGD is the relatively high energy consumption, with the main contribution from the power consumed by circulating pumps in the absorber. In this paper, hybrid modeling and real-time predictive scheduling of a wet flue gas desulfurization (WFGD) system for energy saving and life extension are proposed. The hybrid model is based on the mechanism of the SO2 absorption process and modified based on the operation data using a particle swarm optimization (PSO) algorithm. The prediction model can describe the SO2 absorption process well, and the root-mean-square error (RMSE) of the model is 2.20 mg/m3. Effects of parameters such as the inlet SO2 concentration, pH, flue gas flow rate, and combinations of circulating pumps on the outlet SO2 concentration are further explored. The optimal combination of circulating pumps under different conditions is calculated. Finally, a real-time predictive scheduling strategy is proposed and evaluated under different parameter settings. The system shows the best performance when the upper limit, the target of outlet SO2 concentration, and the lower limit are set to 30, 15, and 10 mg/m3, respectively. The energy consumption of the system is 2564.6 kW, which is 10.6% lower than that before optimization.

Automated summary

This paper proposes a hybrid modeling and real-time predictive scheduling method for a wet flue gas desulfurization (WFGD) system to save energy and extend its lifespan. A prediction model is established based on the mechanism of the SO2 absorption process and modified using a particle swarm optimization (PSO) algorithm with operation data. The optimal combination of circulating pumps is determined under different parameter conditions, and a real-time predictive scheduling strategy is proposed. The system achieves a 10.6% reduction in energy consumption, with an outlet SO2 concentration of 30, 15, and 10 mg/m3 as the upper limit, target, and lower limit, respectively.