Research On Soot Blowing Optimization Problem Of Heating Surface Of Coal-fired Power Station Boiler

Due to the continuous development of China's economy and the deepening of the concept of energy conservation,China has put forward higher requirements for the utilization rate of energy and the control of pollutant emissions.Coal-fired thermal power generation is the most important source of electricity supply in my country.During the combustion of coal-fired power station boilers,ash deposits are inevitably caused on the surfaces of various heating surfaces.This problem can be effectively solved by timely cleaning the dirt on the surface of the heating surface.At present,the soot blowing methods adopted by most domestic thermal power plants are generally based on manual experience to set soot blowing for a fixed time and a fixed duration.This kind of soot blowing operation has certain disadvantages.Therefore,this paper focuses on the problem of soot blowing optimization on the heating surface of coal-fired power station boilers.By formulating reasonable and feasible soot blowing methods to achieve "ondemand soot blowing",so as to improve the energy utilization rate of power plants and meet the urgent needs of energy saving and emission reduction.The research work of this paper mainly includes the following aspects:(1)In this paper,the cleaning factor is used as the characteristic factor to characterize the ash state of the heating surface of the boiler.According to the cleaning factor data collected by the boiler DCS in real time,the real-time ash accumulation state of the heating surface of the boiler is judged,and the lower limit of the cleaning factor is used to guide the soot blowing.Follow-up soot blowing optimization work laid the foundation.(2)This paper first establishes the soot blowing optimization model that minimizes the heat loss per unit time based on the principle of minimum cost,and uses the cleaning factor offline data to analyze and fit the function model that can reflect the ash characteristics of the heating area,and then optimizes to obtain the best soot blowing timing and corresponding soot blowing threshold to guide soot blowing operation.(3)In addition,this paper also proposes an online real-time soot blowing optimization method based on the unscented Kalman filter algorithm,which uses double exponential function fitting to analyze the cleaning factor degradation data,and uses the unscented Kalman filter algorithm to update the model parameters.The curve function of cleaning factor in the future is predicted,and combine with the soot blowing optimization model with the largest heat transfer per unit time to obtain the best soot blowing timing,so as to realize online real-time soot blowing guidance.