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Study on Powder Coke Combustion and Pollution Emission Characteristics of Fluidized Bed Boilers

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Study on Powder Coke Combustion and Pollution Emission Characteristics of Fluidized Bed Boilers energies Article Study on Powder Coke Combustion and Pollution Emission Characteristics of Fluidized Bed Boilers Chen Yang 1,2,*, Haochuang Wu 1,2 , Kangjie Deng 1,2, Hangxing He 3 and Li Sun 1,2 1 Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400030, China; [email protected] (H.W.); [email protected] (K.D.); [email protected] (L.S.) 2 School of Energy and Power Engineering, Chongqing University, Chongqing 400030, China 3 Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, China; [email protected] * Correspondence: [email protected] Received: 22 February 2019; Accepted: 9 April 2019; Published: 13 April 2019 Abstract: The fluidized reactor is widely used in a number of chemical processes due to its high gas-particle contacting efficiency and excellent performance on solid mixing. An improved numerical framework based on the multiphase particle-in-cell (MP-PIC) method has been developed to simulate the processes of gas–solid flow and chemical reactions in a fluidized bed. Experiments have been carried out with a 3-MW circulating fluidized bed with a height of 24.5 m and a cross section of 1 m2. In order to obtain the relationship between pollutant discharge and operating conditions and to better guide the operation of the power plant, a series of tests and simulations were carried out. The distributions of temperature and gas concentration along the furnace from simulations achieved good accuracy compared with experimental data, indicating that this numerical framework is suitable for solving complex gas–solid flow and reactions in fluidized bed reactors. Through a series of experiments, the factors affecting the concentration of NOx and SOx emissions during the steady-state combustion of the normal temperature of powder coke were obtained, which provided some future guidance for the operation of a power plant burning the same kind of fuel. Keywords: fluidized bed; powder coke; MP-PIC method; emission characteristics 1. Introduction The fluidized reactor is widely used in a number of chemical processes (e.g., circulating fluidized bed boiler combustion, biomass pyrolysis, catalytic reaction, and conceptual fluidized bed nuclear reactor [1–4]) due to its high gas-particle contacting efficiency, wide fuel adaptability, and excellent performance on solid mixing. Powder coke, which is also called semicoke or coke powder, was used as fuel in this fluidized bed boiler research. The powder coke used in the experiment was a high-quality Jurassic coal block produced in China’s Shenfu coalfield, which was carbonized and burned in an internally heated vertical retort. Its structure is blocky and its color is light black. It is a fuel and reducing agent for ferroalloys, fertilizers, calcium carbide, blast furnace injection, and other applications. As a new type of fluidized bed boiler fuel, it has a relatively high fixed carbon content; low ash, aluminum, and sulfur contents; and its specific resistance and chemical activity are high. With the slowdown of China’s domestic thermal power market growth, higher requirements for energy savings and emissions reductions for new power plants are required. Thus, vigorous development of circulating fluidized bed (CFB) boiler combustion technology for special fuels has important strategic significance for broadening the future circulating fluidized bed market. In order to study the fuel combustion and emission characteristics of fluidized bed boilers, a large number of experiments have been implemented over the past years to optimize the operating Energies 2019, 12, 1424; doi:10.3390/en12081424 www.mdpi.com/journal/energies Energies 2019, 12, 1424 2 of 18 parameters of these fluidized bed boilers to achieve higher performance and lower costs. Further, extensive mathematical models of multiphase flow and reaction have been developed to explore the gas–solid flow reaction characteristics in fluidized bed boiler furnaces. With the development of technology, the computing power of large computers continues to increase, and numerical methods have become a powerful tool to study the phenomenon of gas-solid fluidized flow reaction. Two theoretical approaches have been proposed to simulate the complex physical and chemical processes in a fluidized reactor, namely, the Eulerian-Eulerian method, also called the two-fluid model (TFM), and the hybrid Eulerian-Lagrangian model (HEL) [5–7]. The two-fluid model treats the solid phase as a pseudocontinuum; an analogy is made between the kinetic theory of granular flow with the molecular kinematic theory of dense gas. The macroscopic characteristics of fluidized reactors can be calculated with relatively small computational cost. This approach has been developed since it was first applied in the simulation of gas–solid flow in a riser by Sinclair and Jackson [8]. However, important parameters, such as particle size distribution and size variation due to reaction or collision, are very hard to describe with this method. Many unclosed terms are also generated during the solid particle averaging procedure, and methods to couple gas-phase turbulence with particle fluctuation still have their limits [9]. The hybrid Eulerian-Lagrangian approach, in which a particle is treated as a discrete element [10,11], is a good way to solve the gas–solid fluidized bed reaction. For the HEL model, each particle, composed of physical (size, temperature, and density) and chemical (reactive) properties, can be tracked individually, and detailed microscopic properties (particle trajectory and transient forces acting on a specific particle), which are extremely difficult to acquire by the TFM method, can also be obtained at the particle level. In this model, the computational cost increases rapidly when the number of particles calculated increases. Therefore, it is very difficult to achieve greater accuracy of the gas-particle flow field by simply adding the number of particles simulated. In order to overcome the contradiction of accuracy and computational cost, the multiphase particle-in-cell (MP-PIC) method is proposed to calculate the properties of particles in dense particle flow [12]. In the MP-PIC method, the solid phase is treated as both a continuum and a discrete phase, the particle stress gradient is calculated on the grid, and the particle properties are mapped from the Lagrangian coordinates to a Eulerian grid by the use of interpolation functions. This approach can attain a good equilibrium between CPU cost and the accuracy of results when simulating industrial-scale fluidized reactors. Based on the MP-PIC method, Snider and Banerjee simulated a heterogeneous catalytic chemistry process in fluidized ozone decomposition and made a comparison between it with experimental results [13]. Abbsabi et al. simulated the 2D physical and chemical performance of the feeding section of a fast fluidized bed steam gasifier [14]. Xie et al. developed a 3D numerical model and used it to simulate coal gasification in a fluidized bed gasifier [11]. Ryan et al. used this method to investigate the performance of a carbon capture fluidized bed reactor [15]. Adamczyk et al. used this approach to model particle transport and combustion in an industrial-scale CFB boiler [16]. Loha et al. made a 3D kinetic simulation of fluidized bed biomass gasification by coupling the chemical reactions with the hydrodynamic calculation of gas–solid flow [17]. Zhong et al. modeled the olive cake combustion process in CFB with the gas flow solved by large eddy simulation [18]. In these studies, the calculated particles were treated as being of spherical shape, which is impossible in reality. Further, the variations of particles during the chemical process, such as fragmentation, shrink, and aggregation, were also neglected in these studies. In this study, an improved numerical framework based on the MP-PIC method was developed to simulate the processes of gas–solid flow and chemical reactions in a fluidized bed. Experiments were carried out on a 3-MW circulating fluidized bed with a height of 24.5 m and a cross section of 1 m2. Simulations and experiments were carried out under different operation conditions. Energies 2019, 12, 1424 3 of 18 2. Mathematical Model/Numerical Method 2.1. Gas-Solid Hydrodynamics 2.1.1. Gas Phase The gas-phase mass and momentum equations are as follows. Gas-phase continuum equation: Ng @ "gρg X + "gρgug = S (1) @t r · gi i=1 N Pg where Sgi is the sum of the mass changes of each component due to the chemical reaction, and i is i=1 the number of gas-phase species. Momentum equation: @ "gρgug + "gρgugug = "g p + "gτg + "gρgg + β us ug (2) @t r · − r r · − where β is the gas–solid drag coefficient [19]. 8 2 > "s µg + "sρg <> 150 2 2 1.75 " d ug us "g < 0.8 β = "g ds g s − (3) > 3 "sρg 2.65 :> Cd ug us "g− "g 0.8 4 ds − ≥ 8 > 24 0.687 <> Re 1 + 0.15Res Re < 1000 Cd = > (4) :> 0.44 Re 1000 ≥ 2ρ f ug us rs Re = − (5) µg where µg is the molecular gas viscosity. Energy equation: @("gρghg) + "gρgugh = @t r · f @p h i . (6) "g + ug p + F "g λg Tg + Q + S + q @t · r − r · − r h D . where F represents the viscous dissipation term, Q represents the energy source term, and λg represents gas fluid thermal conductivity. qD represents enthalpy diffusion, written as Ng . X q = h "gρgD Y (7) D r · i r g,i i=1 2.1.2. Solid Phase Each component solid-phase particle was mapped to a discrete form by statistical weighting, and the m-th component particle having the same particle diameter Dm and density ρm was replaced by Nm calculated particles (i.e., parcels). For the M groups of fuel particles, the number of real particles N was calculated by; the spatial distribution of the parcels over time was (i) (i) (i) (i) (i) (i) (i) (i) (i) (i) f Xp (t), up (t), Dp , ρp , Wp , i = 1, ..., N ; and Xp (t),up (t),Dp ,ρp , and Wp were the position, Energies 2019, 12, 1424 4 of 18 velocity, diameter, density, and statistical weight of a parcel.
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