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
Problems associated with polynuclear aromatic hydrocarbon (PAH) contaminated site in environmental media have received increasing attention. To resolve such problems, innovative in situ methods are urgently required. This work investigated the feasibility of using surfactants to extract phenanthrene on spiked sand in a batch system. Phenanthrene was spiked into Ottawa sand to simulate contaminated soil. Six surfactants, Brij 30 (BR), Triton X-100 (TR), Tergitol NP-10 (TE), Igepal CA-720 (IG), sodium dodecyl sulfate (SDS) and hexadecyl trimethyl ammonium bromide (HTAB) were used. Adjusting the extraction time, mixing speed and surfactant concentration yielded the optimum extracting conditions. The concentration of phenanthrene was identified with HPLC. Under the experimental conditions, results indicated that those surfactants were highly promising on site remediation since the residual phenanthrene concentration was effectively reduced. The optimum operating conditions were obtained at 30 min, 125 rpm and surfactant concentrations in 4%. 相似文献
Studies of heavy metals and organic pollutants in different benthic mussel species from Bohai Sea show that concentrations of Cd in mussels commonly exceed national biological quality standards. In addition, a site located in Laizhou Bay exhibits higher average concentrations of As, Hg and Pb with respect to the other sites. Residual levels of petroleum hydrocarbons at several sites in Liaodong Bay also exceed quality guidelines. Contents and compositional characteristics of DDT and its metabolites in mussels suggest the probability of recent inputs and potential ecological risks to the local benthic environment. 相似文献