A new concept of a supercritical water （SCW） circulating fiuidized bed reactor is proposed to produce hydrogen from coal/biomass gasification. The cyclone is a key component of the reactor system, in this paper, cyc...A new concept of a supercritical water （SCW） circulating fiuidized bed reactor is proposed to produce hydrogen from coal/biomass gasification. The cyclone is a key component of the reactor system, in this paper, cyclones with a single circular inlet （SCI） or a double circular inlet （DCI） were designed to adapt to the supercritical conditions. We evaluated the separation performance of the two cyclones using numerical simulations. A three-dimensional Reynolds stress model was used to simulate the turbulent flow of the fluid, and a stochastic Lagrangian model was used to simulate the particle motion. The flow fields of both cyclones were three-dimensionally unsteady and similar to those of traditional gas-solid cyclones. Secondary circulation phenomena were discovered and their influence on particle separation was estimated. Analyzing the distribution of the turbulence kinetic energy revealed that the most intensive turbulence existed in the zone near the vortex finder while the flow in the central part was relatively stable. The particle concentration distribution was non-uniform because of centrifugal forces. The distribution area can be divided into three parts according to the motion of the particles. In addition, the separation efficiency of both cyclones increased with the inlet SCaN velocity. Because of its perturbance flow, the DCI separator had higher separation efficiency than the SCI separator under comparable simulations. However, this was at the expense of a higher pressure drop across the cyclone.展开更多
A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation N...A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as selforganizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boiIers, which are under research by the authors.展开更多
文摘A new concept of a supercritical water （SCW） circulating fiuidized bed reactor is proposed to produce hydrogen from coal/biomass gasification. The cyclone is a key component of the reactor system, in this paper, cyclones with a single circular inlet （SCI） or a double circular inlet （DCI） were designed to adapt to the supercritical conditions. We evaluated the separation performance of the two cyclones using numerical simulations. A three-dimensional Reynolds stress model was used to simulate the turbulent flow of the fluid, and a stochastic Lagrangian model was used to simulate the particle motion. The flow fields of both cyclones were three-dimensionally unsteady and similar to those of traditional gas-solid cyclones. Secondary circulation phenomena were discovered and their influence on particle separation was estimated. Analyzing the distribution of the turbulence kinetic energy revealed that the most intensive turbulence existed in the zone near the vortex finder while the flow in the central part was relatively stable. The particle concentration distribution was non-uniform because of centrifugal forces. The distribution area can be divided into three parts according to the motion of the particles. In addition, the separation efficiency of both cyclones increased with the inlet SCaN velocity. Because of its perturbance flow, the DCI separator had higher separation efficiency than the SCI separator under comparable simulations. However, this was at the expense of a higher pressure drop across the cyclone.
文摘A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as selforganizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boiIers, which are under research by the authors.
