Coal blending is a process of optimization under all constraints.The influences of different constraints on the objective function are different.To analyze the effects of several constraints including coal calorific value

volatile

moisture

ash and sulfur content on the optimal price of coal blending

an optimizing model was established by using particle swarm optimization algorithm in this paper.Moreover

the effects of adjusting the constraint range of calorific value and volatile content of the coal on coal blending economy were analyzed in detail.The results show that

the influence of each constraint on the blending coal's price meets the piecewise linear function

the economy of range constraint is determined by the slope of the function

the smaller absolute value of the slope has the higher economy.Setting reasonable constraints for coal blending can further improve the economy of blending coal and satisfy the requirement of coal property.The research methods in this paper can provide reference for constraints design of coal blending

it is helpful for thermal power plants to obtain more economical solution of coal blending.