Optimization Strategy For Imperfect Soot Blowing Of Coal-fired Boiler Based On Accelerated Degradation Model

Coal-fired power station boilers will cause ash accumulation on the heating surface due to the combustion of pulverized coal.The use of soot blowers can effectively deal with this problem.At present,most coal-fired power stations use timed and quantitative methods for soot blowing based on human experience.Artificial soot blowing has great randomness,and it is easy to misjudge the degree of soot in the clean area,resulting in too frequent soot blowing or insufficient soot blowing,which leads to waste of resources.So it is necessary to optimize the soot blowing method of the boiler and formulate a reasonable blowing method.The ash optimization strategy enables coal-fired power plants to obtain better economic benefits.Therefore,this article focuses on this subject,taking the economizer heating surface as the research object to conduct an in-depth study on the optimization of boiler soot blowing.The main research contents are as follows:(1)This paper takes the cleaning factor as the characteristic parameter of the ash state of the heated area,which directly reflects the degree of ash accumulation on the heated surface,and establishes an ash pollution monitoring model combined with related thermodynamic formulas to obtain the calculation formula of the cleaning factor.At the same time,wavelet domain denoising is used to preprocess the problems in the thermal data collected by the DCS system,and the processed cleaning factor data is used as the data source for the calculation of the example in this article,which provides theoretical guidance for the subsequent chapters.(2)Based on the real-time online acquisition of the cleaning factor of the heating surface,this paper proposes a DELM prediction-based optimization method for boiler soot blowing from the perspective of the net income of soot blowing under the premise of safe operation of the boiler unit.Use the DELM prediction algorithm to update and predict the cleaning factor data in real time,predict the future change trend of the cleaning factor,and use the Fourier and second-order exponents to fit the curve functions of the soot section and the soot blowing section,respectively.Taking the maximum net income of soot blowing on a heating surface per unit time as the objective function,a soot blowing optimization model based on the principle of solving the maximum value of the objective function is established to provide theoretical guidance for the reasonable operation of soot blowers.(3)After each soot blowing action,the soot accumulation on the heating surface is accelerated.To solve this problem,this paper proposes a dynamic soot blowing optimization research method with the minimum cost of soot blowing,and establishes a method based on Gamma degradation.Accelerated dust accumulation model on the heated surface of the process.According to the different status of each soot blowing and the change of soot accumulation speed,different soot blowing methods are used to optimize the soot blowing operation.The PSO optimization algorithm is used to obtain the best soot blowing time and cleaning factor soot blowing threshold.Minimizing the soot blowing cost rate provides a certain reference for the economic operation of coal-fired boiler power plants.