Optimization Of Soot Blowing Of Coal-fired Boilers Based On Prediction Of Health Status Of Heated Surfaces

China’s outline of the 13 th five-year plan for national economic and social development puts forward new requirements and indicators for energy conservation and emission reduction.Thermal power generation is the mainstay of China’s power generation industry.Its use of resources and pollutant emissions are very huge,energy-saving and emission reduction in the thermal power generation industry is imminent.The combustion process of coal-fired power station boilers inevitably produces ash pollution.As the flue gas flows,it forms on the heating surface to form ash and slag,which causes the heat transfer efficiency of the heated surface to decrease and the flue blocked.It will reduce power generation efficiency,increase coal consumption,pollute the environment and even cause safety accidents.Therefore,timely purging of the ash and slag on the heated surface is one of the important means to ensure the safe and efficient production of coal-fired power stations.In order to solve the problem of soot ash optimization of coal-fired power stations,firstly,the ash pollution of the heating surface of coal-fired power stations is monitored,and then based on the efficient production of boilers.An off-line data,particle-based filtering combined with real-time data and soot-based randomness-based soot-blowing optimization control scheme are proposed to meet the urgent needs of the high-efficiency operation of coal-fired power plants and energy saving and emission reduction.The main research work of this paper includes the following contents:Firstly,the ash pollution monitoring of the heating surface of coal-fired power stations was studied.Traditional direct monitoring methods are more difficult to implement on site,and the soft-measurement method was adopted to monitor the deposition of ash pollution on the heating surface of coal-fired power station boilers.That is to say,the cleaning factor is used as a characteristic factor to characterize the ashing state of the heated surface,and by establishing a grey pollution monitoring model combined with relevant thermodynamics and energy balance calculation methods to obtain cleaning factors.It laid the foundation for the study of the subsequent soot optimization strategy.Secondly,the formulation of the soot blowing optimization strategy is the main content of this paper.Firstly,the research is based on the static soot blowing optimization model.The historical data of a large number of coal-fired power stations are used to obtain the data from the ash deposition to the soot blowing.In a soot blowing cycle,it can reflect the change of the function curve over a period of time,in order to improve the operating efficiency of the coal-fired power station,establish a soot-blowing optimization model with maximum heat transfer per unit time in a soot blowing cycle,and optimize the solution to obtain the best The timing of soot blowing lays the foundation for the next step.Third,for the real-time dynamic ash deposition process,the soot optimization strategy of real-time optimal soot blowing point in a soot blowing period from coal-fired power station boiler to soot blowing period is studied.The particle filter algorithm combines with real-time data to predict and acquire the real-time cleaning factor curve in a single soot cycle.Then,the minimum soot loss per unit time is taken as the goal to establish the soot blowing optimization strategy.The optimal algorithm is used to calculate the best soot blowing point and soot blowing time.Secondly,the problem is that the data volume is large and the data usefulness is different.The method of applying weights divides the data into the first half and the second half,and adjusts the weights so that the finally obtained clean factor change function more accurately reflects the predicted partial change trend.Provides guidance for the development of a soot blowing optimization strategy on a single soot cycle.Fourth,for the problem of independent incremental randomness of ash deposition in the heating period of coal-fired power station boiler heating surface,this paper adopts the idea and method of predicting state based on fault prediction and health management technology to a 300 MW coal-fired power station.The boiler economizer is the research object of this paper.The cleaning factor is used as the characteristic factor to characterize the health status of the heated surface,and the update process is used to realize the randomness of the health status of each heated surface.At the same time,the update theorem is used,combined with the constraints of working conditions.An optimization model with monitoring function interval,preventive soot threshold as the optimization variable and the lowest operating cost of coal-fired boilers as the objective function is established.Finally,taking a 300 MW coal-fired power station as an example,the particle swarm optimization algorithm is used to solve the problem.The solution results prove the feasibility of the proposed optimization model and provide theoretical guidance for the formulation of the soot blowing optimization strategy for coal-fired power station boilers.