Chungen Yin

Modelling the motion of cylindrical particles in a nonuniform flow

The models currently used in computational fluid dynamics codes to predict solid fuel combustion rely on a spherical shape assumption. Cylinders and disks represent a much better geometrical approximation to the shape of bio-fuels such as straws and woods chips. A sphere gives an extreme in terms of the volume-to-surface-area ratio, which impacts both motion and reaction of a particle. For a nonspherical particle, an additional lift force becomes important, and generally hydrodynamic forces introduce a torque on the particle as the centre of pressure does not coincide with the centre of mass. Therefore, rotation of a nonspherical particle needs to be considered. This paper derives a model for tracking nonspherical particles in a nonuniform flow field, which is validated by a preliminary experimental study: the calculated results agree well with measurements in both translation and rotation aspects. The model allows to take into account shape details of nonspherical particles so that both the motion and the chemical reaction of particles can be modelled more reasonably. The ultimate goal of such a study is to simulate flow and combustion in biomass-fired furnaces using nonspherical particle tracking model instead of traditional sphere assumption, and thus improve the design of biomass-fired boilers.

Further study of the gas temperature deviation in large-scale tangentially coal-fired boilers☆

Gas temperature deviation in upper furnace is an important but a less reported issue in large-scale tangentially fired boilers, since they endanger largely boilers operation. Simulations are conducted in this paper to study the deviation. Perfect agreement between the simulation results and key boiler design values and available site operation records indicates that the calculations are reliable. Based on the simulations, effect of some factors, including residual airflow swirling at furnace exit, super-heaters panels, coal particle trajectories and their combustion histories, on temperature deviations are studied in details. The most important cause and how it affects the temperature deviation are located. Two new methods, a nose on front-wall and re-arranged super-heater panels, are put forward unprecedentedly to alleviate the deviations.

Co-firing straw with coal in a swirl-stabilized dual-feed burner: modelling and experimental validation.

This paper presents a comprehensive computational fluid dynamics (CFD) modelling study of co-firing wheat straw with coal in a 150kW swirl-stabilized dual-feed burner flow reactor, in which the pulverized straw particles (mean diameter of 451microm) and coal particles (mean diameter of 110.4microm) are independently fed into the burner through two concentric injection tubes, i.e., the centre and annular tubes, respectively. Multiple simulations are performed, using three meshes, two global reaction mechanisms for homogeneous combustion, two turbulent combustion models, and two models for fuel particle conversion. It is found that for pulverized biomass particles of a few hundred microns in diameter the intra-particle heat and mass transfer is a secondary issue at most in their conversion, and the global four-step mechanism of Jones and Lindstedt may be better used in modelling volatiles combustion. The baseline CFD models show a good agreement with the measured maps of main species in the reactor. The straw particles, less affected by the swirling secondary air jet due to the large fuel/air jet momentum and large particle response time, travels in a nearly straight line and penetrate through the oxygen-lean core zone; whilst the coal particles are significantly affected by secondary air jet and swirled into the oxygen-rich outer radius with increased residence time (in average, 8.1s for coal particles vs. 5.2s for straw particles in the 3m high reactor). Therefore, a remarkable difference in the overall burnout of the two fuels is predicted: about 93% for coal char vs. 73% for straw char. As the conclusion, a reliable modelling methodology for pulverized biomass/coal co-firing and some useful co-firing design considerations are suggested.

Predicting coal ash fusion temperature with a back-propagation neural network model

A novel technique, the back-propagation (BP) neural network, is presented for predicting the ash fusion temperature from ash compositions for some Chinese coals instead of the traditional techniques, such as the ternary equilibrium phase diagrams and regression relationships. In the applications of the BP networks, some modifications to the original BP algorithm are adopted to speed up the BP learning algorithm, and some useful advice is put forward for the choice of some key parameters in the BP model. Compared to the traditional techniques, the BP neural network method is much more convenient and direct, and can always achieve a much better prediction effect.

Methods to improve prediction performance of ANN models

Abstract Artificial neural network (ANN) is a powerful tool and applied successfully in numerous fields. But there are still two limitations on its use. One is over-training, which occurs when the capacity of the ANN for training is too great because it is allowed too many training iterations. The other is that ANNs are not effective for extrapolation, which is sometimes very important because the existing data used to train an ANN do not necessarily cover the entire range. The two limitations degrade seriously the prediction performance of ANN models. In this paper, two practices are introduced to alleviate or overcome the negative effect of the limitations. Demonstrations based on these practices indicate that they are general and useful practices and can improve greatly the prediction performance of the resulting ANN models to make them really suitable for engineering applications.

A Novel Non-Linear Programming-Based Coal Blending Technology for Power Plants

Coal blending has now attracted much attention in coal industry of China, and has been investigated extensively to meet the often conflicting goals of environmental requirements and reliable and efficient boiler operation in power plants. However, most of the existing blending projects are guided by experience, or linear-programming (LP), whose main assumption is that all the quality parameters of a blend can be approximated as the weighted average of the corresponding indexes of its component coals at any condition. This has been proved incorrect for some blend properties. Now, more and more evidence indicates that a strong non-linearity exists between some quality parameters of a coal blend and those of its component coals. Thus the unreliable assumption impairs the resulting coal-blending scheme. To remedy this situation, a novel coal blending technology for power plants, i.e. using nonlinear programming (NLP) based on neural network models, was proposed, and has now been successfully applied at the Hangzhou Coal Blending Center. The application attests that this new technology is much better than the existing linear-programming coal-blending method.

Advanced CFD modelling of air and recycled flue gas staging in a waste wood-fired grate boiler for higher combustion efficiency and greater environmental benefits

Grate-fired boilers are commonly used to burn biomass/wastes for heat and power production. In spite of the recent breakthrough in integration of advanced secondary air systems in grate boilers, grate-firing technology needs to be advanced for higher efficiency and lower emissions. In this paper, innovative staging of combustion air and recycled flue gas in a 13 MWth waste wood-fired grate boiler is comprehensively studied based on a numerical model that has been previously validated. In particular, the effects of the jet momentum, position and orientation of the combustion air and recycled flue gas streams on in-furnace mixing, combustion and pollutant emissions from the boiler are examined. It is found that the optimized air and recycled flue gas jets remarkably enhance mixing and heat transfer, result in a more uniform temperature and velocity distribution, extend the residence time of the combustibles in the hot zone and improve burnout in the boiler. Optimizing the air and recycled flue gas jet configuration can reduce carbon monoxide emission from the boiler by up to 86%, from the current 41.0 ppm to 5.7 ppm. The findings of this study can serve as useful guidelines for novel design and optimization of the combustion air supply and flue gas recycling for grate boilers of this type.

Biomass co-firing

Abstract: Co-firing biomass with fossil fuels in existing power plants is an attractive option for significantly increasing renewable energy resource utilization and reducing CO2 emissions. This chapter mainly discusses three direct co-firing technologies: pulverized-fuel (PF) boilers, fluidized-bed combustion (FBC) systems, and grate-firing systems, which are employed in about 50%, 40%, and 10% of all the co-firing plants, respectively. Their basic principles, process technologies, advantages, and limitations are presented, followed by a brief comparison of these technologies when applied to biomass co-firing. This chapter also briefly introduces indirect co-firing and parallel co-firing and their application status.

The fractal dimension of calcined limestone and its sulfur-removal reactivity

A method based on fractal geometry is suggested to simulate the porous structure of calcined limestone, in which a model similar to the Menger sponge is employed for the porous calcine. The bulk fractal dimension is calculated on the basis of the experimentally obtained pore-size distribution density. The effects of various factors (such as particle size, calcination temperature and environment, etc.) on the porous structure (i.e. on its bulk fractal dimension) are studied. Combined with the results of sulfur-removal experiments and theoretical analysis, it is concluded that optimum utilization will be achieved when limestone is calcined to a structure with bulk fractal dimensions between 2.53 and 2.65. This fractal dimension is calculated under normal conditions and may provide an alternative ranking index for calcium-based sorbents.

Development of CFD-based icing model for wind turbines: A case study of ice sensor

Operation of wind turbines in cold climate areas is challenged by icing-induced problems, such as loss of production, safety issues and blade fatigue. Production losses are especially a big issue in Sweden, and due to difficulties with on-site measurements, simulations are often used to get an understanding and to predict icing events. In this paper a case study of modeling icing using Computational Fluid Dynamics(CFD) is proposed. The case study aims to form the basic of a general CFD model for icing on wind turbine blade sections.