Prediction Of Fouling On Heating Surface And Soot Blowing Optimization In Coal-fired Power Plant Boiler

Achieving the safe,clean and efficient production of coal-fired power plant boilers has always been an important issue for energy conservation and emission reduction in China.However,the fouling of the heating surface in coal-fired power plant boiler seriously affects its heat transfer efficiency and directly leads to a drop in the operating efficiency of the entire unit.The soot blowing operation is an effective solution to the problem of fouling on the heated surface of the boiler,but the current fixed soot blowing method relies on manual experience,and there are many unreasonable situations.In order to solve the problem of low soot blowing efficiency on the heating surface of coal-fired power plant boilers,this article conducts online monitoring and prediction of the ash status on the heated surface,combined with the maximization criterion of soot blowing net income,and proposes soot blowing optimization strategies to improve the operating efficiency of the boiler.The research content of this article includes the following aspects:Firstly,the problem of online acquisition of key parameters during the operation of coal-fired power plant boilers is being studied.Aiming at the problem of large fluctuations in the thermal data collected by the internal sensors of the boiler,wavelet filtering is used to preprocess data.In view of the inability to fully cover the power station site measurement points,and the problem that some measurement points require regular maintenance and cannot provide real-time data,a soft measurement method based on energy balance is adopted to obtain the required key parameters.Aiming at the problem that the key thermodynamic parameters required for modeling are difficult to obtain online,a database of thermodynamic properties on the working fluid side and the flue gas side is established to meet the needs of real-time calling.The experimental results show that the proposed wavelet filtering,soft measurement based on energy balance,and the method of establishing a physical property database realize the online acquisition and denoising of key internal parameters of the boiler,and provide the basis for the subsequent soot blowing optimization research.Secondly,in view of the current problem of lagging monitoring of fouling on the heating surface of the boiler,this paper selects the thermal efficiency coefficient as the ash deposition characterization parameter of the radiant heating surface,and use inverse heat balance method to monitor fouling trend online;the ash accumulation characteristic parameter is selected as the ash accumulation characteristic parameter of the convective heating surface,and use the HGWO-SVM model to predict the cleaning heat absorption of the heating surface,and combined with the actual heat absorption,the ash fouling monitoring on the convective heating surface is realized.Thirdly,based on the monitoring of fouling on heating surface in coal-fired power plant boiler,the research on the online prediction method of ash accumulation is carried out.Aiming at the problem of long delay in guiding the soot blowing operation with fouling monitoring data,use LSTM network to make short-term prediction of the gray status of the heated surface,and the soot blowing operation is guided based on the monitoring and prediction data to improve the soot blowing efficiency.Experimental verification shows that the error of the prediction model is less than 5%,and the prediction time is within 30 seconds,which meets the demand for online prediction.Finally,in order to improve the economic benefits of the boiler soot blowing process,establish soot blowing loss model and profit model,combined with heating surface fouling monitoring,prediction data and boiler operating conditions.Based on the basic principles of soot blowing,formulate a soot blowing optimization strategy based on maximizing the net income of soot blowing,and give the best soot blowing cycle and soot blowing time.It is convenient for the on-site staff to observe the status of soot deposits on each heated surface and suggestions for soot blowing,and implement soot blowing operations in time.The optimized strategy is compared with the traditional strategy to verify that the optimized soot blowing strategy can effectively improve the boiler operating efficiency and reduce pollution emissions.