Life Prediction And Management Of SCR Denitration Catalyst

Selective catalytic reduction(SCR)is the most widely used nitrogen oxide removal technology in thermal power plants.Its core is SCR denitration catalyst.It is urgent to predict the health and residual life of the catalyst in the SCR decontamination system in the scientific maintenance and safe use of the flue gas denitrification system.At present,the method of predicting the lifetime of SCR catalyst mainly uses the exponential model and the single factor inactivation kinetic model established by analyzing the deactivation principle.However,the working environment of the catalyst is complicated,the flue gas conditions are changing.Only the influence of the single factor on the catalyst cannot accurately predict the inactivation law,and cannot analyze the service life.The catalyst activity is a physical property of catalyst and does not change with the operating and flue gas conditions,which decreases as the catalyst slowly inactivates itself.Therefore,this paper chooses the catalyst activity as the prediction parameter.Because it has the gray properties of the sample data for the catalyst activity and the service time,grey model BP neural network model and two gray neural network model are established to avoid the influence of the scarceness and variability of sample on the prediction results.Finally,an example is given to show that the direct output method in gray neural network has high accuracy and can provide some references for the life estimation and replacement management of SCR denitrification catalyst in coal-fired power plants.Finally,with ammonia slip amount as the evaluation criteria,the life management model for specific denitrification conditions is established based on the basic model of SCR reactor and the deactivation model of catalyst.Combining catalyst replacement or supplement situation and life management model,12 feasible catalyst management schemes are calculated and compared to obtain the optimal one,which can provide a basis for catalyst management in thermal power plants.