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Separation performance of fine low-rank coal by vibrated gas-solid fluidized bed for dry coal beneficiation 被引量:5
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作者 Jingfeng He Yuemin Zhao Jie Zhao Zhenfu Luo Chenlong Duan Yaqun He 《Particuology》 SCIE EI CAS CSCD 2015年第6期100-108,共9页
Vibrational energy was introduced to a dense medium gas-solid fluidized bed to improve the separation performance of 1-6 mm fine low-rank coal. The setup was termed a vibrated gas-solid fluidized bed and could provide... Vibrational energy was introduced to a dense medium gas-solid fluidized bed to improve the separation performance of 1-6 mm fine low-rank coal. The setup was termed a vibrated gas-solid fluidized bed and could provide a stable fluidization state and uniform density distribution for dry coal beneficiation by the transfer of vibrational energy and the interaction between vibrations and the gas phase. Favorable segregation of the ash content of the 1-6-ram-sized lignite samples is achieved under suitable operating conditions. Higher yields of cleaning coal were acquired when the ash content was reduced. The probable error values were 0.065 and 0.055 at separating densities of 1.68 and 1.75 g/clTl3 for the 1-3- and 3-6-mm- sized lignite samples, respectively. Effective beneficiation of 1-6-ram-sized fine lignite could be achieved using the vibrated gas-solid fluidized bed, which provides an alternative technique for the separation of fine low-rank coal in arid areas. 展开更多
关键词 LIGNITE Vibrational energyGas-solid fluidized bedSegregationSeparation performance
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Static and Transient Performance Prediction for CFB Boilers Using a Bayesian-Gaussian Neural Network
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作者 HaiwenYe WeidouNi 《Journal of Thermal Science》 SCIE EI CAS CSCD 1997年第2期141-148,共8页
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
关键词 Bayesian-Gaussian neural network back-propagation neural network circulating fluidized bed boiler performance prediction
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Cyclone separation in a supercritical water circulating fluidized bed reactor for coallbiomass gasification: Structural design and numerical analysis 被引量:7
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作者 Guoxing Li Youjun Lu 《Particuology》 SCIE EI CAS CSCD 2018年第4期55-67,共13页
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
关键词 CycloneStructural design Supercritical water-solid flow Separation performance Circulating fluidized bed Computational fluid dynamics
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