On the basis of the common fact that the current coal deviated from the design coal in power plants

an optimized coal blending model for power plants was established by using quantum-behaved particle swarm optimization algorithm.In this model

the coal blending economy and coal property parameter were regarded as the object functions

and six indices including price

calorific value

ash content

volatile matter content

moisture content and sulfur content of the coal were taken as constraint conditions.According to the received coal in a power plant in Inner Mongolia

this model was adopted to perform the optimization calculation for coal blending.The simulation result showed that

compared with the particle swarm algorithm with inertia weight

the quantum particle swarm algorithm had better global search capability and astringency

it can quickly search the optimal coal blending ratio and the most suitable price.