Study Of Intelligent Prediction Method Of Condenser Fouling

Condenser a large transfer auxiliary equipment in the powers, chemical,machinery and other fields plays an essential part in thermal power plants for itsproviding the cold source for the thermodynamic cycle in the large steam turbine.Therefore, the performance of its work has direct influence on the economy andsecurity in the entire steam turbine operation process. However, once the condenser isput into operation, there will be some fouling inside the condenser, which will do harmto the operation of condensers. In recent years many scholars from home and broadhave proposed a lot of new technologies of predicting fouling in order to solve theproblems discussed above and offer the basis for washing. The paper chooses thecondenser scaling as the main research object, its modeling and predicting method insoft measurement has been studied.Firstly, this paper explained the cause of fouling accumulation, the reason why itinfluences the performance of condenser, the important significance of its prediction,working principle and the status of prediction for it.Secondly, the prediction model of fouling in condenser is instructed thoroughlyaccording to its formation mechanism, classification and each phase when it is forming.Then, the overall fouling is separated into two parts soft fouling and hard fouling,which can be respectively got by phased prediction and mathematical statistics model,and the final fouling factor can be attained by adding up them together. Though, theconventional measurement methods of fouling are widely used, they have their ownshortcoming from the point of view of economics and accuracy. This paper designs anonline fouling prediction strategy based on K-means algorithm and Chebyshev neuralnetwork to conquer the problem discussed above, and introduces the multiple inputsingle output (MISO) models instead of the traditional dynamic single input singleoutput (SISO) Chebyshev neural network to adapt to the condenser fouling which isaffected by various of condition parameters. The fouling factor is largely influenced byworking conditions and big disturbance in the process of prediction, so sampling datapretreatment, which controls the data under the limit of the threshold to maintain thecontinuity of fouling data, is given to keep the prediction value in the default precisionand equal the actual value. At last the gray neural networks based on gray models andneural networks is proposed for fouling prediction because of Grey models and neuralnetworks has their own characteristics. In this model, gray models play a main role rather than neural network. By contrast, the simulation results show that gray modelscan achieve more accurate fouling prediction than traditional fouling models underdifferent work conditions in the short-term prediction process.Finally, we simulate the proposed algorithms to verify the validity and feasibilityof the algorithms. The results show that the algorithms are effective and feasible.