This paper focuses on sulfur removal technologies in industrial grate furnaces (IGF) and pulverized coal fired boilers (PCFB) with high flame temperature of 1200-1600 ℃. The SO2 reduction without sorbents during coal combustion, thermal stabilities of sulfation products, kinetics of sulfur retention reactions of sorbents, desulfurization processes, and sulfur removal under unconventional atmospheres at high temperature are reviewed. It is proposed that some powdered minerals or industrial wastes with effective metal components may be used as sorbents for sulfur removal to promote cost effectiveness. Because the main reason that results in low desulfurization efficiencies in IGF and PCFB is the thermal decomposition of the conventional sulfation product CaSO4 above 1200 8C, it is key to explore new sulfation products that are thermally stable at high temperatures. It is also necessary to study the kinetic catalysis of alkali and transitional metal compounds on sulfation reactions under the combustion conditions of IGF and PCFB. The two-stage desulfurization process, in which SO2 is captured by sorbents both in the coal bed and the combustion gas, is promising for IGF, especially with the humidification of flue gas in a water-film dust catcher. The staged desulfurization process combined with air-staged combustion, in which sorbents are injected into the primary air field and upper furnace to capture SO2 under reducing and oxidizing atmospheres, is promising for PCFB. Flue gas recirculation is also an effective desulfurization process under O2/CO2 conditions and can give a high desulfurization efficiency of about 80% in furnaces.

A two-dimensional particle population balance model has been developed for the particle size and density distributions in a circulating fluidized bed (CFB) boiler furnace based on analysis of particle properties and the core-annulus hydrodynamic model. The model, incorporating modules to consider fuel particle fragmentation, char combustion, particle attrition and gas-solid separation, is part of an overall model developed earlier by the authors to simulate the operation of a 12MW CFB boiler. The model predictions for particle population in a CFB furnace agree well with the measurement data.

Large-scale vortex structures and their effects on the dispersion of particles in turbulent free shear flows are very important in many industrial applications, such as combustion, pollution control, and materials processing. In order to understand large-scale vortex structures and particle dispersion in depth, as well as their interaction effects, a two-way-coupled three-dimensional mixing layer laden with particles at a Stokes number of 5 initially located in the upper half region is studied numerically. A pseudospectral method was used to directly simulate the flow fluid, and the Lagrangian approach was used to trace particles. The concept of computational particles is introduced to vary the mass loading of particles. The momentum coupling effect introduced by a particle approximates to a point force. The simulation results show that coherent structures are still dominant in the mixing layer, but the flow dynamics and particle dispersion are modulated. The length of large-scale vortex structures is shortened and the pairing is delayed. Higher mass loading results in lower energy of the fluid in the phase of Kelvin-Helmholtz rolling up, while in the pairing process of large-scale vortex structures, the energy of the fluid increases as the mass loading increases. Higher mass loading also leads to larger mixed fluid thickness and Reynolds stresses of the flow. In addition, the particle dispersion along the transverse direction differs from that along the spanwise direction, which indicates that the effects of the addition of a particle on the spanwise large-scale vortex structures are different from those on the streamwise large-scale vortex structures.

In this work, an effective method based on artificial neural network (ANN) and genetic algorithms (GA) is suggested for modeling the carbon burnout behavior in a tangentially fired utility boiler and optimizing the operating conditions to achieve the highest boiler heat efficiency consecutively. When carbon burnout behavior under various operating conditions are experimentally investigated, the comparison between the output of ANN modeling and the experimental data shows satisfactory agreement. A genetic algorithm is employed to perform a search to determine the optimum solution of the neural network model, identifying appropriate setpoints for the current operating conditions.

The present work introduces an approach to predict the nitrogen oxides (NOx) emission characteristics of a large capacity pulverizedcoal fired boiler with artificial neural networks (ANN). The NOx emission andcarbon burnout characteristics were investigatedthroug h parametric fieldexperi ments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt were studied. On the basis of the experimental results, an ANN was used to model the NOx emission characteristics andthe carbon burnout characteristics. Comparedwi th the other modeling techniques, such as computational fluid dynamics (CFD) approach, the ANN approach is more convenient and direct, and can achieve good prediction effects under various operating conditions. A modified genetic algorithm (GA) using the micro-GA technique was employedto perform a search to determine the optimum solution of the ANN model, determining the optimal setpoints for the current operating conditions, which can suggest operators' correct actions to decrease NOx emission.