Advances in air pollution control for vessels in China.

Vessel emissions have contributed a great deal to air quality deterioration in China. Hence, the Chinese government has promulgated a series of stringent emission regulations. It is in this context that vessel emission control technology research is in full swing. In particular, during the 13th Five-Year Plan, the air pollution control technology of vessels has greatly improved. Vessel emission control has followed two main governance routes: source emission reduction and aftertreatment technology. Source control focuses on alternative fuels, with two main directions, the development of new fuels and the modification of existing fuels. Moreover, after-treatment technologies have also been developed, including wet desulfurization technology using seawater or alkaline liquids as wet washing liquids and selective catalytic reduction (SCR) for the control of NOx emission. Due to China's increasingly stringent emissions standards and regulations, work on the development of clean alternative fuels and further upgrading the collaborative application of after-treatment technologies to meet the near-zero-emissions requirements of vessels is still necessary.

Dynamic NOx emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers.

An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx emission concentration at the outlet of boilers on different operating conditions to support the DeNOx procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOx concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOx concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R2 of the composite LSTM model are 3.53 mg/m3 and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m3 and 0.78, respectively).

A real-time optimization method for economic and effective operation of electrostatic precipitators.

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. IMPLICATIONS: 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.

Evaporation and concentration of desulfurization wastewater with waste heat from coal-fired power plants.

The reduction of wet desulfurization wastewater is one of the important tasks of coal-fired power plants, and it is important for achieving "zero emissions." Evaporation and concentration (E&C) with waste heat is an effective way to reduce wastewater. Here, two typical types of industrial desulfurization wastewater are used to study the change rule of pH and total dissolved solids during wastewater concentration in a circulating evaporation tower. The results indicate that with the increase of concentration ratio, the pH of desulfurization wastewater is decreased rapidly and then is gradually stabilized at 2-3 when SO2 or SO3 is contained in flue gas, and the increase in conductivity is less for wastewater with higher SO42- content. The characteristics of various ions are also analyzed, and the composition and microscopic morphology of the precipitates are characterized during concentration. The growth pattern of Ca2+ concentration is dependent on the ratio of Ca2+ and SO42- in raw wastewater. When the concentration ratio is 7.21, the insoluble and slightly soluble substances undergo precipitation and the solid content is approximately 20%, which can help realize the concentration and reduction of desulfurization wastewater.

Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method.

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.

Experimental study on the evaporation and chlorine migration of desulfurization wastewater in flue gas.

Wastewater from a limestone-gypsum wet desulfurization system cannot be directly reused or discharged due to its high suspended matter content and complex water composition. Desulfurization wastewater evaporation in flue gas is an effective way to dispose wastewater. Multicomponent soluble chlorine salts exist in the desulfurization wastewater. During the evaporation, chlorine enters into the flue gas due to volatilization, which accelerates the enrichment rate of the Cl- concentration in the desulfurization slurry and leads to an increase in wastewater production. This study explored the chlorine migration of various chlorine salt solutions and typical desulfurization wastewater at high temperature during the evaporation process of concentrated wastewater by a laboratory-scale tube furnace and a pilot-scale system. Results showed that when NaCl-evaporated substance was heated, the chlorine ion hardly volatilized. For the evaporated substances of CaCl2 and MgCl2 solutions, some of the crystal water was lost, and hydrolysis occurred to generate gaseous HCl. NH4Cl was easily sublimed, and the decomposition temperature was lowest. A pilot study on spray evaporation of desulfurization wastewater in flue gas showed that the particle size of the evaporated product increased and the main particle size was within 2.5-10 µm with increasing flue gas temperature. Increasing the mass ratio of gas to liquid significantly reduced the particle size of the atomized particles, thereby reducing the average particle size of the evaporated particles. The HCl concentration increased with increasing flue gas temperature. When the flue gas temperature was 350 °C, the concentration of HCl was 40 ppm, and the escape rate of chlorine in the desulfurization wastewater was approximately 30% using typical wastewater from a limestone-gypsum wet desulfurization system.