This paper is particularly related to elemental mercury (Hg0) oxidation and divalent mercury (Hg2+) reduction under simulated flue gas conditions in the presence of nitric oxide (NO) and sulfur dioxide (SO2). As a powerful oxidant and chlorinating reagent, Cl2 has the potential for Hg oxidation. However, the detailed mechanism for the interactions, especially among chlorine (Cl)-containing species, SO2, NO, as well as H2O, remains ambiguous. Research described in this paper therefore focused on the impacts of SO2 and NO on Hg0 oxidation and Hg2+ reduction with the intent of unraveling unrecognized interactions among Cl species, SO2, and NO most importantly in the presence of H2O. The experimental results demonstrated that SO2 and NO had pronounced inhibitory effects on Hg0 oxidation at high temperatures when H2O was also present in the gas blend. Such a demonstration was further confirmed by the reduction of Hg2+ back into its elemental form. Data revealed that SO2 and NO were capable of promoting homogeneous reduction of Hg2+ to Hg0 with H2O being present. However, the above inhibition or promotion disappeared under homogeneous conditions when H2O was removed from the gas blend. 相似文献
Sulfur dioxide (SO2) is one of the main air pollutants from many industries. Most coal-fired power plants in China use wet flue gas desulfurization (WFGD) as the main method for SO2 removal. Presently, the operating of WFGD lacks accurate modeling method to predict outlet concentration, let alone optimization method. As a result, operating parameters and running status of WFGD are adjusted based on the experience of the experts, which brings about the possibility of material waste and excessive emissions. In this paper, a novel WFGD model combining a mathematical model and an artificial neural network (ANN) was developed to forecast SO2 emissions. Operation data from a 1000-MW coal-fired unit was collected and divided into two separated sets for model training and validation. The hybrid model consisting a mechanism model and a 9-input ANN had the best performance on both training and validation sets in terms of RMSE (root mean square error) and MRE (mean relative error) and was chosen as the model used in optimization. A comprehensive cost model of WFGD was also constructed to estimate real-time operation cost. Based on the hybrid WFGD model and cost model, a particle swarm optimization (PSO)-based solver was designed to derive the cost-effective set points under different operation conditions. The optimization results demonstrated that the optimized operating parameters could effectively keep the SO2 emissions within the standard, whereas the SO2 emissions was decreased by 30.79% with less than 2% increase of total operating cost.
Implications: Sulfur dioxide (SO2) is one of the main pollutants generated during coal combustion in power plants, and wet flue gas desulfurization (WFGD) is the main facility for SO2 removal. A hybrid model combining SO2 removal mathematical model with data-driven model achieves more accurate prediction of outlet concentration. Particle swarm optimization with a penalty function efficiently solves the optimization problem of WFGD subject to operation cost under multiple operation conditions. The proposed model and optimization method is able to direct the optimized operation of WFGD with enhanced emission and economic performance. 相似文献