RBF (Radial Basis Function) neural network based supercritical boiler nitric oxide discharging dynamic predication method
An ultra-supercritical boiler and neural network technology, applied in the biological neural network model, etc., can solve the problems that do not meet the sensitivity and accuracy requirements of modeling auxiliary variables, and cannot accurately represent the instantaneous pulverized coal amount.
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[0031] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.
[0032] The test object of the present invention is Guohua Xuzhou Power Generation Co., Ltd. SG3099 / 27.46-M545 type ultra-supercritical parameter variable pressure operation spiral tube coil direct current furnace. The SCR in the process of increasing the load from 500MW to 1000MW within 24 hours, and then reducing from 1000MW to 500MW Reactor inlet NOx content (dry basis standard state, 6% O2 state).
[0033] Before model training, the invalid data set locked in the probe purge stage should be eliminated first, and the data set should be normalized to the range of [-1,1] according to formula (12), and then restored according to Equation (13) is denormalized.
[0034] x'=(2x-x max -x min ) / (x...
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