Abstract: As the main power consumption equipment in the limestone-gypsum wet flue gas desulfurization system of thermal power plants, optimizing the operation of the slurry circulation pump is of great practical significance to reduce power consumption and improve economic efficiency of the power plant. In this paper, according to the current operation status of slurry circulation pumps in thermal power plants, an operation optimization strategy of slurry circulation pumps based on historical operating data is proposed incorporated with the combination of clustering and classification method. Specifically, the fuzzy C-means clustering algorithm and the combinatory evaluation method based on the weighted fusion of analytic hierarchy process and entropy weight method are applied to automatically search the optimal historical operation of the slurry circulation pump under different operating conditions, such that the SVM classifier is trained with the best historical operation record. Then by using the genetic algorithm for parameter optimization and the real-time operation data as input, to implement the recommended operations of the slurry circulation pump, the optimization of its operation can be achieved. The simulation experiments based on the onsite measurement data from a power plant show that by using this slurry circulation pump operation optimization method, the power consumption of the slurry circulation pump can be reduced by about 21.55%, which can provide the kind of valuable reference for energy saving and emission reduction in thermal power plants.

Key words: wet flue gas desulfurization, optimization of slurry circulation pump operation, fuzzy C-means clustering, analytic hierarchy process, entropy weight method