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Research of IMC Algorithm Applied in pH Value Control System for Flue Gas Resulfurization of Coal-Fired Boiler

Author: LiuTianYuan
Tutor: ZhangChangSheng
School: Kunming University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Flue gas desulfurization pH control Radial basis function neural network Internal Model Control
CLC: TP273
Type: Master's thesis
Year: 2009
Downloads: 53
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China 's fuel structure , coal , coal accounted for 75 % of total energy production and consumption . SO2 from coal combustion is the main characteristic of China 's air pollution , which most harmful dust and acid rain . Caused by SO2 emissions of acid rain pollution hazard area reaches more than 30% of the land area , and the degree of pollution intensifies , a direct result of the national economic losses amounting to hundreds of billions . The SO2 pollution has become an important factor restricting China's economic and social sustainable development , to control the pollution is imperative . Coal-fired boiler flue gas desulfurization process , the pH value of the control process in the absorber is a typical non-linear and lags . Radial basis function neural network with a single hidden layer of three-layer feedforward network , it simulates the human brain neural network structure of the receiver domain local adjustments and cover each other , to approximate any continuous nonlinear function with arbitrary precision ability , adaptive and self- learning ability and complex uncertain problems . The internal model control is a very practical method of control , its main feature is simple structure , requiring a lower level of accuracy of the model , and the robustness and stability of the system has been greatly improved , and can eliminate the unmeasured disturbance on the impact of the system . In this paper, radial basis function neural network internal model control algorithm to study the desulfurization of the pH value and process control , and explore a method suitable for the application of wet flue gas desulfurization . Through in-depth study of the pH of the absorber desulfurization process , analyze the characteristics of the absorber slurry pH . Draw the corresponding mathematical model using least squares identification method based on Hammerstein model . Use of the MATLAB simulation methods designed controller , RBF neural network applied to the internal model control , an advanced control method by radial basis function neural network to identify forward model and inverse model of the internal model control , making the internal model control has better adaptive capacity , increase the scope of the internal model control applications . By system simulation results showed that the desulfurization pH values ??and systems nonlinear internal model control algorithm based on RBF neural network , the pure delay system has good control performance , tracking ability , adaptive ability and robustness.

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