Energy Consumption Characteristics Of Exhaust Gas Temperature And Soot Blowing Optimization Under All Working Conditions Considering Safety And Economy

In recent years,the country has vigorously carried out the strategic goal of"energy-saving priority".As one of the large energy consumers.thermal power units must shoulder the heavy responsibility of energy conservation and emission reduction.The heat loss of boiler exhaust gas is the biggest energy loss of boiler,which is affected by the temperature and flow rate of boiler exhaust gas.In order to reduce the heat loss of exhaust gas,it is necessary to reduce the exhaust gas temperature as much as possible,improve the thermal efficiency of the boiler,and then reduce the coal consumption rate of generating units.The possible influencing factor of boiler exhaust temperature higher than the design value is the accumulation of ash on the heating surface of the boiler.In this paper,the cleaning factor model is used to monitor the extent of ash accumulation and apply it to intelligent soot blowing,so as to achieve the purpose of reducing the exhaust temperature.In this paper,under the condition of considering safety and economy,the exhaust temperature under different working conditions was optimized and the energy consumption characteristics were analyzed.From the perspective of intelligent soot blowing optimization,the phenomenon of excessively high exhaust temperature in power plant was solved and the soot blowing economy was analyzed,which is of great significance to the energy saving and consumption reduction of coal-fired power plant boilers.The acid dew point was calculated based on seven acid dew point calculation methods so that the optimal range of exhaust smoke temperature was higher than the acid dew point to ensure that the air preheater would not suffer low temperature corrosion.Based on the boiler efficiency counter balance method to calculate boiler efficiency and energy consumption characteristics analysis was carried out on the exhaust temperature,characteristic variables selection based on stepwise regression algorithm,based on the BP neural network to establish characteristic variables and the boiler efficiency model,based on the gray Wolf algorithm to optimize the BP neural network model in different working condition of discharge smoke temperature optimization to maximize efficiency of the boiler to ensure efficiency.It is of great significance to conduct in-depth optimization of exhaust temperature from two perspectives of safety and economy.Combined with the actual value and optimization value of exhaust temperature,it is of great significance to analyze the energy consumption characteristics of exhaust temperature,which is of great significance to guide operators to adjust parameters and to conduct operation team assessment of power plant.For the unit whose exhaust temperature is higher than the reference value,real-time intelligent soot blowing should be carried out from the perspective of operation to improve the heat transfer coefficient of the heating surface and reduce the exhaust temperature.In order to ensure the safety of boiler heating surface pipeline to improve the boiler thermal efficiency under the premise,this article selects the installation of pipe blowing loss small acoustic blowing system of ningxia as the research object,a coal-fired power plant boiler cleaning factor model is established,based on the grey Wolf algorithm optimization support vector machine(SVM)model for surface cleaning degree online monitoring and provide theoretical basis for boiler blowing in real time,The economics of soot blowing is analyzed from two aspects of the effect of soot blowing on the efficiency of boiler and turbine.Based on the algorithm and calculation program in this paper,I participated in the completion of the data mining part of the consumption difference analysis system of a power plant of a group and the intelligent soot blowing optimization software of a plant.The implementation of the two softwares provides a breakthrough point for the construction of intelligent power plants in China.