Mixed Structure-radial Basis Function Neural Network Optimal Control on Spraying Ammonia Flow for Supercritical Boiler Flue Gas Denitrification

Spraying ammonia flow can influence the efficiency of supercritical boiler's flue gas denitrification device based on selective catalytic reduction(SCR).Excessive spraying flow can also result in ash deposit and corruption of backward heating units such as air heater,simultaneously,it causes resource waste and second pollution.Moreover,optimal traditional PID control with variational load on the flow is difficult.And in order to improve traditional radial basis function(RBF) neural network(RBFNN)'s adaptivities of nonlinearity and disturbance during variational working condition,so,a new control scheme based on mixed structure RBFNN(MS-RBFNN) was proposed.This MS-RBFNN can synthetically study current main relative state parameters,so as to parallel calculate the optimal spraying ammonia flow by using least NOx discharge of SCR device as its training signal.Experimental results indicate,comparing with traditional PID control,this scheme's advantages on better NOx control effect and adaptability of variable working condition as well as little ammonia usage.