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Neural Network-Based Intelligent Integrated Modeling for the CFB-FGD Processes

Author: ZhangXiaoBin
Tutor: LiHongRu
School: Northeastern University
Course: Control Theory and Control Engineering
Keywords: Circulating fluidized bed flue gas desulfurization Mechanism model Intelligent integration model Neural Networks Modeling
CLC: X701.3
Type: Master's thesis
Year: 2005
Downloads: 176
Quote: 3
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Abstract


Circulating fluidized bed flue gas desulfurization (CFB-FGD) technologies to the circulating fluidized bed principle is based, by repeatedly recycling the reaction product and the desulfurizing agent, an increase of the concentration of the material within the reactor, the reaction time is extended, so that a substantial increase in the utilization and the desulfurization efficiency of the desulfurizer, is a low-investment, high desulfurization efficiency, reliable operation and operation of flue gas desulphurization technology and easy maintenance etc.. China's coal-fired power generation in the electricity supply, accounting for more than 80%. Coal-dominated energy consumption structure has led to a large number of sulfur dioxide emissions, have had a serious impact on China's ecological environment. Conserve resources, reduce pollution emissions is one of the important issues that need to be resolved in China. The technically feasible indicators, not engineered, shorter construction cycle, more reasonable investment for research and development with independent intellectual property rights in China coal-fired power plant CFB-FGD control technology, this paper to the national \focus on key topics \CFB-FGD system is a non-linear multi-variable complex system, not yet fully grasp the complexity of the mechanism, the mechanism point of departure to establish the mathematical model can not fully meet the requirements of the CFB-FGD system, which leads to enlarged guidance of the design as well as industrial production process is still mainly based on empirical. In addition to the establishment and maintenance of the mathematical model is very complex mathematical model takes a long time in the calculation of the iterative CFB-FGD system in order to resolve these difficulties, a neural network-based intelligent integrated modeling. First of all, under the guidance of the theory of intelligent integrated modeling, focusing on the integration between the neural network and the traditional modeling methods, a weighted formula before input to the neural network intelligence integrated modeling methods, and gives its concrete realization. The method to predict high precision, faster convergence, suitable for nonlinear serious, especially more industrial process input variables. Then, in-depth analysis of the laws governing the operation of the CFB-FGD process, the establishment of a mechanistic model of the CFB-FGD. Include: the drying stage desulfurization reactor model, the gas-solid stage desulfurization model, bag filter desulfurization model and consider the case material backmixing desulfurization model in drying stage model contains a ring / nuclear flow model. To test the feasibility of the process model, the steady state will be established mechanism model calculated values ??and actual data for comparison of the results showed that the model is better able to simulate the actual steady-state operating state of the system. Finally, according to the CFB-FGD mechanistic model to determine the factors that affect the desulfurization efficiency, various factors determine the weighting coefficients of the size of the contribution by the desulfurization efficiency, CFB-FGD system based on the intelligent integration of the neural network model. And this model structure, training and simulation capabilities, a thorough and systematic study. The simulation results show that the intelligent integration model of the neural network can be used to simulate and predict the desulfurization efficiency; better and mechanistic models, smart integration model of the neural network of the CFB-FGD.

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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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