The Research On Approaches Of Intelligent Measurement And Control For Fouling Cleaning In Condenser

For the purpose of improving the heat transfer performance of condenser and increasing the efficiency of thermal cycle of turbo-generator unit, this dissertation investigates the approaches of intelligent measurement and control for fouling cleaning in condenser. The main topics of the dissertation are described as follows.Firstly, the dissertation explains the necessity of fouling monitoring and fouling cleaning by investigating the cause of fouling accumulation and the influence on heat transfer performance of condenser, and analyzes the advantage and disadvantage of the ordinary fouling measurement and cleaning methods. Then, basic idea and main methods of intelligent technology are introduced and a conclusion is made, which demonstrates the application of intelligent technology in fouling measurement and control is an effective way for improving the heat transfer performance of condenser.A novel strategy which combines high pressure water cleaning with chemical cleaning is proposed in this dissertation. Based on the strategy, a cleaning system is developed and the detail of the control system is described.A novel approach based on feature selection and intelligent modeling for precise measurement of fouling is proposed. In the approach, sensitivity calculation based on BP neural network is introduced to determine the most suitable feature variable which reflects the fouling state. T-S model, multi-RBF Neural network and diagonal recurrent neural network are employed to separate the influences of both fouling and other factors, such as off-design condition on feature variable. Based on it, an experimental system is developed and experiment on an actual condenser is carried out. The results show the approach measures the fouling correctly, and is more effective than thermal resistance method or heat transfer coefficient method under the condition of blocked tubes or excessive amount of air in condenser.The prediction of fouling in condenser is heavily influenced by the periodic fouling processes and dynamics change of the parameters. To deal with it, a novel prediction approach based on intelligent technology is proposed. Based on it, experiments are conducted. The results show the approach achieves fouling prediction precisely and is more effective than asymptotic fouling model and improved asymptotic fouling model.