Modeling And Optimization On The Denitration Cost Of Power Unit Boilers

Large coal-fired power plant boiler has high operating costs when using the flue gas denitration technology, and exploring an online-optimizing method will enhance the competitiveness of power enterprise. This paper mainly considers the modeling and optimization problem in the denitration process of power units,and then proposes a real operation data-based denitration cost optimization system that guides operators in economically adjusting the operation parameters of boilers to reduce the denitration cost.A data-driven least square support vector machine (LSSVM) learning method is utilized to predict the denitration cost of a coal-fired boiler. Back propagation (BP) is used here to select the input variables to simplify the model and improve its accuracy. With the pre-built BP-LSSVM-based denitration cost model, the genetic algorithm (GA) is then applied to offline optimizations at the frequently operating load points to find the optimal adjustment variables(AVs), which results in an Offline Optimal Expert Database (OOED). Once a load command is received, fuzzy association rule mining (FARM) is employed to extract the relationship between the operating load point and the optimal adjustment variables (AVs) in the OOED, thereby achieving the online denitration cost optimization of the power plant.For comparison, a single LSSVM method is also employed to build a denitration cost prediction model, and the GA and FARM proposed in this study are compared too. The results show that, compared with the single LSSVM method, the BP-LSSVM method not only predicts more accurately but also lowers the model complexity. In addition, considering the denitration cost, optimization time, and update time, the proposed GA-FARM-based denitration cost optimization system is always better than traditional optimization methods, and is very suitable for the online optimization of denitration cost of power unit boilers.