The main cost of coal-fired power plants comes from coal consumption, and, in recent years, strict requirements for NOx emissions make denitrification an increasingly important part of operating costs. Therefore, the establishment of an effective integrated model of boiler combustion and denitrification is the basis for power plants economic optimization. In this paper, historical operation data are selected from the supervisory information system (SIS) of a 990MW thermal power plant. Combining the improved adaptive GA with the least squares support vector machine (LSSVM), the input variables are selected by the adaptive genetic algorithm (GA) to reduce the dimension and complexity of the model. The selected variables are used as the input of the LSSVM model and a GA-LSSVM model for a boiler combustion and denitrification integrated system is established. Comparing the model with the simple LSSVM model, the simulation results show that the complexity of integrated model can be effectively reduced by variable selection, the generalization ability of the model can be improved and the modeling time can be reduced. The integrated model can predict the SCR efficiency, SCR outlet NOx concentration and boiler efficiency accurately and rapidly.
|Title of host publication||2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)|
|Publication status||Published - 23 Nov 2017|
|Event||2017 IEEE 14th International Conference on e-Business Engineering - |
Duration: 4 Jun 2017 → 6 Nov 2017
|Conference||2017 IEEE 14th International Conference on e-Business Engineering|
|Period||4/06/17 → 6/11/17|