State Monitoring And Optimization Of Direct Air Cooling System

Dozens of axial fans of the direct air cooling system consume a lot of energy,and cluster effect and ambient wind may change the working point of the fan at a certain position and back pressure of the unit,lead to the rise of the net coal consumption rate.Therefore,it is of great significance to study performance monitoring of cold-end system and the reasonable adjustments of fan group for a direct air-cooled system under variable conditions considering cluster effect and ambient wind.Taking a certain 600 MW direct air-cooled power plant as research object,considering variable ambient wind conditions,applying CFD simulation and BP neural network,a model of the cold-end system is established and the economic back pressure of the unit is studied.First of all,the direct air cooling system is established based on a numerical simulation model,studing the relationship between the temperature of the fin tube and the air absorption under different conditions.To speed up the calculation speed of the numerical model,an iterative algorithm is proposed based on the least squares.The flow of each fan and the back pressure of the two direct air cooling units are calculated.The accuracy of the numerical model of the cold end system is verified by comparing the model calculation results with the actual operation data of the direct air-cooled unit.Based on CFD simulation and BP neural network,the characteristic model of direct air cooling system is established,which provides a model basis for the prediction of the back pressure of the direct air-cooled system under variable conditions.Secondly,based on the numerical model of air cooling island,the distributions of air flow and temperature fields are analyzed under the conditions of no wind,cross wind,post furnace air and pre furnace air.The results show that the cluster factor and air temperature at the inlet of each fan are strongly related to its space location,ambient wind speed and direction.Subsequently,based on the characteristic model of cold end in the direct air cooling system,thermal parameters are exported under various working conditions,and the influences of ambient wind on the performance of cold end are studied.Crosswind mainly affects back pressure of the windward direct air cooling system.Wind coming from the back of boiler house would have great effects on back pressures of the two systems,while wind coming from the front of boiler house would have little impact.Furthermore,based on the operation optimization theory of the direct air cooling system,the optimum back pressure model,considering the cluster effect and ambient wind,is established by combining the CFD simulations and BP neural network.The optimum back pressure and fan speed are obtained under various working conditions of steam turbine,and the guidance for optimal operation of the direct air cooling system is provided.To make up the cooling capacity shortage of direct air cooling system under high temperature and strong wind conditions,another row of air-cooled condenser cells are utilized.The results show that the optimum back pressure and fan rotation speed increase with exhaust steam load and/or ambient temperature.Ambient temperature has a different impact on the relations of ambient wind to optimum back pressure and fan rotation speed.Ambient wind has a negative influence on the maximum net power generation.After cooling capacity expansion of the direct air cooling system,back pressure has an obvious decrease,so the operation economy gets improved.Finally,the software of performance monitoring and optimization of the direct air cooling system is developed based on Visual Studio.Net platform and SQL Server data-base.