A novel denitration cost optimization system for power unit boilers

Reducing the denitration cost of coal-fired boilers is important to enhance the competitiveness of power generation companies. This study proposes a real operation data-based denitration cost optimization system that guides operators in economically adjusting the operation parameters of boilers. 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. With the pre-built BP-LSSVM-based denitration cost model, the genetic algorithm (GA) is then applied to obtain offline optimizations at the typical operating load points, 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 adjustable 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 BP-LSSVM-GA-FARM-based denitration cost optimization system is always better than traditional optimization methods. (C) 2016 Elsevier Ltd. All rights reserved.