Boiler soot blowing optimization method based on heating surface health state prediction

Abstract

The invention discloses a boiler soot blowing optimization method based on heating surface health state prediction. The boiler soot blowing optimization method is characterized in that drum-type prediction for characteristic change of cleaning factors of a soot deposition section in a single soot blowing period is achieved through a filtering algorithm, a functional expression is acquired, a unitminimum heat transfer energy consumption soot blowing optimization model in the single soot blowing period is utilized, and a single soot blowing optimization strategy at the optimal soot blowing timepoint in the single soot blowing period is acquired. According to the boiler soot blowing optimization method, the problem that making of the soot blowing optimization strategy based on big data is inaccurate on single soot blowing operation is solved, and with a 300-MW coal-fired power plant boiler adopted as an example, the boiler soot blowing optimization method has relatively good economy andscientificity and has a guiding function for soot blowing strategy making of a coal-fired power plant.

Description

technical field [0001] The invention relates to the field of soot blowing optimization on heating surfaces of boilers in coal-fired power plants, in particular to a boiler soot blowing optimization method based on prediction of the health status of the heating surfaces. Background technique [0002] Electric energy is one of the most important energy sources in human civilization. At present, most of my country's electricity production comes from coal-fired power generation. However, during the combustion of coal, ash and slagging will be generated on the heating surface of the power plant boiler, which will reduce the heat transfer efficiency of the boiler and cause energy waste. In severe cases, it may even endanger the safe operation of the unit. Therefore, in order to maximize the utilization of coal combustion energy, the heating surface of the boiler pipe must be cleaned and blown in time. Only by formulating the most reasonable and scientific soot blowing optimizatio...

AI Technical Summary

Problems solved by technology

In this paper, multiple sets of historical data are used to obtain the mean value and then fit the method to obtain the change curve of the cleaning factor of the ash accumulation section. There is a certain difference between the change of the cleaning factor of the ash accumulation section and the change of the statistical mean value, so the formulation of the soot blowing optimization strategy accurate to a single time is not accurate enough

Images