Abstract
Targeting at the selective catalytic reduction (SCR) de-NOx system of a coal-fired unit, a NARX-neural-network-based dynamic data modeling method was proposed. Firstly, analyzed the main factors affecting the operation characteristics of SCR de-NOx system. According to the characteristics of operational data, insignificant variables were excluded to simplify the model input variables. Secondly, the dynamic characteristics of the SCR de-NOx system near an equilibrium point were described by using a linear multivariable model of second-order inertia plus pure delay. The nonlinearity of the system was reflected in the change of linear model parameters. Based on this principle, determined the order and the pure lag time of input variables. Lastly, a NARX-neural-network model was developed on the basis of the actual operating data and the validity of the model was confirmed in comparison with the operation data of the SCR de-NOx system of the coal-fired unit.
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