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Soft-sensor Method of Circulating Ash Utilization in CFB-FGD Process Based on RBF Neural Network

Author: YangZuo
Tutor: LiHongRu
School: Northeastern University
Course: Control Theory and Control Engineering
Keywords: Circulating fluidized bed flue gas desulfurization Circulating ash utilization Soft Measurement Improved particle swarm optimization algorithm Radial basis function neural network
CLC: X701.3
Type: Master's thesis
Year: 2008
Downloads: 67
Quote: 0
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The circulating fluidized bed flue gas desulfurization ( CFB - FGD ) technology is a new semi-dry flue gas desulfurization , desulfurization efficiency of wet process approach or reach the lower calcium to sulfur ratio case . Desulfurization products are easy to handle , equipment, small footprint , reliable operation , easy operation and maintenance , low investment costs . This paper first introduces the mechanism of CFB - FGD and a state-of-the art . CFB - FGD mechanism and process that many circulating ash recycling greatly improving the efficiency of circulating fluidized bed flue gas desulfurization . The specific role of the material cycle process is not very clear , the circulating ash utilization measurement is conducive to further research circulating fluidized bed flue gas desulfurization , and to achieve optimal control of dry flue gas desulfurization system . At present there is little research literature about the role of circulating ash in the desulfurizer , this article is the role played by further research circulating fluidized bed desulfurization process cycle gray , soft methods of measurement of circulating ash utilization . The choice of auxiliary variables has a crucial role in the establishment of the soft sensor model , this paper, circulating ash utilization factors detailed analysis of detailed mechanistic analysis to select the appropriate variables do auxiliary variables . The amount of water spray is selected , fresh the desulfurization dose , the amount of circulating ash , inlet gas concentration , inlet flue gas flow , the inlet flue gas temperature of these six variables do the auxiliary variables of the model . Particle swarm optimization algorithm, introduced decreases linearly inertia weight and constriction factor , improved particle swarm optimization ( MPSO ) . MPSO algorithms and gradient descent method , MPSO algorithm global search capability and radial basis function neural network ( RBF ) local optimization efficient Blending overcome the ordinary PSO algorithm converges instability and RBF network easy to fall into the local the disadvantage of small value , MPSO - RBF RBF neural network hybrid optimization algorithm . Training RBF neural network soft sensor model for circulating ash utilization , soft circulating ash utilization measurement, simulation results show that the soft sensor model based on RBF neural network has higher precision , better performance and a good prospect .

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CLC: > Environmental science, safety science > Processing and comprehensive utilization of waste > General issues > Exhaust gas processing and utilization > Desulfurization and desulfurization
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