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Electrostatic precipitators (ESP) have been considered as the main particulate matter (PM) removal facility in the energy industry. This paper presents a real-time optimization method for a one-chamber industrial ESP in an ultra-low emission power plant with an intelligent optimization system (IOS). The IOS seeks to optimize the energy consumption of ESP subject to the outlet concentration requirement in real-time. A coordination control logic is designed to regulate the optimized operation set points with varying operation conditions. The operation optimized by the IOS is compared with the operations under PID (proportion-integral-derivative) and manual control. The results show that the IOS improves the emission compliance rate from 95% of manual control to 100% and the medium concentration is tuned to be 46.6% closer to the emission target. Furthermore, a good balance between emission and energy consumption is achieved, with 35.50% energy conservation for the same emission upper limit of 30 mg/m3. These results prove that the IOS significantly contributes to the efficient operation and economic PM removal by ESP for the energy industry. Electrostatic precipitators (ESP) is one of the main PM removal facilities in coal-fired power plants. An intelligent optimization system (IOS) with prediction, optimization, and control modules is designed and constructed for the ESP in an ultra-low emission power plant. A PM removal model is used to predict the outlet concentration of the ESP. The optimal energy consumption of ESP subject to the outlet concentration requirement problem is solved by the particle swarm optimization. A closed-loop and rapping tolerant method is used to eliminate the fluctuation in time-averaged concentration. The system raised is able to ensure the compliance rate while decreasing the energy consumption of the ESP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pollutant abatement systems are widely applied in the coal-fired power sector, and the energy consumption is considered an important part of the auxiliary power. An energy consumption analysis and assessment model of pollutant abatement systems in a power unit was developed based on the dynamic parameters and technology. The energy consumption of pollutant abatement systems in a 1000-MW coal-fired power unit that meets the ultra-low emission limits and the factors of operating parameters, including unit load and inlet concentration of pollutants, on the operating power were analyzed. The results show that the total power consumption of the pollutant abatement systems accounted for 1.27% of the gross power generation during the monitoring period. The wet flue gas desulfurization (WFGD) system consumed 67% of the rate, whereas the selective catalytic reduction (SCR) and electrostatic precipitator (ESP) systems consumed 8.9% and 24.1%, respectively. The power consumption rate of pollutant abatement systems decreased with the increase of unit load and increased with the increase of the inlet concentration of pollutants. The operation adjustment was also an effective method to increase the energy efficiency. For example, the operation adjustment of slurry circulation pumps could promote the energy-saving operation of the WFGD system. Implications: The application of pollutant abatement technologies increases the internal energy consumption of the power plant, which will lead to an increase of power generation costs. The real-time energy consumption of the different pollutant abatement systems in a typical power unit is analyzed based on the dynamic operating data. Further, the influence of different operating parameters on the operating power of the system and the possible energy-saving potential are analyzed. [ABSTRACT FROM AUTHOR]